Coarse grain coherency

ABSTRACT

One embodiment provides for a general-purpose graphics processing device comprising a general-purpose graphics processing compute block to process a workload including graphics or compute operations, a first cache memory, and a coherency module enable the first cache memory to coherently cache data for the workload, the data stored in memory within a virtual address space, wherein the virtual address space shared with a separate general-purpose processor including a second cache memory that is coherent with the first cache memory.

RELATED APPLICATION

The present application is a continuation of and claims the benefit ofU.S. patent application Ser. No. 16/441,499, filed Jun. 14, 2019, nowissued as U.S. Pat. No. 10,657,618 on May 19, 2020, which claims thepriority of U.S. patent application Ser. No. 15/482,810, filed on Apr.9, 2017, now issued as U.S. Pat. No. 10,373,285 on Aug. 6, 2019, whichis incorporated by reference in its entirety.

FIELD

Embodiments relate generally to data processing and more particularly todata processing via a general-purpose graphics processing unit.

BACKGROUND OF THE DESCRIPTION

Current parallel graphics data processing includes systems and methodsdeveloped to perform specific operations on graphics data such as, forexample, linear interpolation, tessellation, rasterization, texturemapping, depth testing, etc. Traditionally, graphics processors usedfixed function computational units to process graphics data; however,more recently, portions of graphics processors have been madeprogrammable, enabling such processors to support a wider variety ofoperations for processing vertex and fragment data.

To further increase performance, graphics processors typically implementprocessing techniques such as pipelining that attempt to process, inparallel, as much graphics data as possible throughout the differentparts of the graphics pipeline. Parallel graphics processors with singleinstruction, multiple thread (SIMT) architectures are designed tomaximize the amount of parallel processing in the graphics pipeline. Inan SIMT architecture, groups of parallel threads attempt to executeprogram instructions synchronously together as often as possible toincrease processing efficiency. A general overview of software andhardware for SIMT architectures can be found in Shane Cook, CUDAProgramming, Chapter 3, pages 37-51 (2013) and/or Nicholas Wilt, CUDAHandbook, A Comprehensive Guide to GPU Programming, Sections 2.6.2 to3.1.2 (June 2013).

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the presentembodiments can be understood in detail, a more particular descriptionof the embodiments, briefly summarized above, may be had by reference toembodiments, some of which are illustrated in the appended drawings. Itis to be noted, however, that the appended drawings illustrate onlytypical embodiments and are therefore not to be considered limiting ofits scope.

FIG. 1 is a block diagram illustrating a computer system configured toimplement one or more aspects of the embodiments described herein;

FIG. 2A-2D illustrate parallel processor components, according to anembodiment;

FIG. 3A-3B are block diagrams of graphics multiprocessors, according toembodiments;

FIG. 4A-4F illustrate an exemplary architecture in which a plurality ofGPUs is communicatively coupled to a plurality of multi-core processors;

FIG. 5 illustrates a graphics processing pipeline, according to anembodiment;

FIG. 6 illustrates a heterogeneous processing system havingheterogeneous hardware coherency, according to an embodiment;

FIG. 7 illustrates logical structures used for coarse grain coherency,according to an embodiment;

FIG. 8 is a flow diagram of logic to enable coarse grain coherency in aheterogeneous processing system;

FIG. 9 is a block diagram of a data processing system, according to anembodiment;

FIG. 10 is a block diagram of a processing system, according to anembodiment;

FIG. 11 is a block diagram of a processor according to an embodiment;

FIG. 12 is a block diagram of a graphics processor, according to anembodiment;

FIG. 13 is a block diagram of a graphics processing engine of a graphicsprocessor in accordance with some embodiments;

FIG. 14 is a block diagram of a graphics processor provided by anadditional embodiment;

FIG. 15 illustrates thread execution logic including an array ofprocessing elements employed in some embodiments;

FIG. 16 is a block diagram illustrating a graphics processor instructionformats according to some embodiments;

FIG. 17 is a block diagram of a graphics processor according to anotherembodiment;

FIG. 18A-18B illustrate a graphics processor command format and commandsequence, according to some embodiments;

FIG. 19 illustrates exemplary graphics software architecture for a dataprocessing system according to some embodiments;

FIG. 20 is a block diagram illustrating an IP core development system,according to an embodiment;

FIG. 21 is a block diagram illustrating an exemplary system on a chipintegrated circuit, according to an embodiment;

FIG. 22 is a block diagram illustrating an additional graphicsprocessor, according to an embodiment; and

FIG. 23 is a block diagram illustrating an additional exemplary graphicsprocessor of a system on a chip integrated circuit, according to anembodiment.

DETAILED DESCRIPTION

In some embodiments, a graphics processing unit (GPU) is communicativelycoupled to host/processor cores to accelerate graphics operations,machine-learning operations, pattern analysis operations, and variousgeneral purpose GPU (GPGPU) functions. The GPU may be communicativelycoupled to the host processor/cores over a bus or another interconnect(e.g., a high-speed interconnect such as PCIe or NVLink). In otherembodiments, the GPU may be integrated on the same package or chip asthe cores and communicatively coupled to the cores over an internalprocessor bus/interconnect (i.e., internal to the package or chip).Regardless of the manner in which the GPU is connected, the processorcores may allocate work to the GPU in the form of sequences ofcommands/instructions contained in a work descriptor. The GPU then usesdedicated circuitry/logic for efficiently processing thesecommands/instructions.

Graphics processors are being increasingly used for general-purposecompute tasks. Example uses of general-purpose GPU (GPGPU) processinginclude but are not limited to machine learning, video analytics, facerecognition, and autonomous vehicle control. GPGPU processing can bemade more efficient via the use of heterogeneous memory systems in whichhardware-managed coherency is enabled between a host processor (e.g.,CPU) and a GPU. Heterogeneous hardware CPU and GPGPU coherency cansignificantly simply the programming model used to enable heterogeneouscomputing.

In the following description, numerous specific details are set forth toprovide a more thorough understanding. However, it will be apparent toone of skill in the art that the embodiments described herein may bepracticed without one or more of these specific details. In otherinstances, well-known features have not been described to avoidobscuring the details of the present embodiments.

System Overview

FIG. 1 is a block diagram illustrating a computing system 100 configuredto implement one or more aspects of the embodiments described herein.The computing system 100 includes a processing subsystem 101 having oneor more processor(s) 102 and a system memory 104 communicating via aninterconnection path that may include a memory hub 105. The memory hub105 may be a separate component within a chipset component or may beintegrated within the one or more processor(s) 102. The memory hub 105couples with an I/O subsystem 111 via a communication link 106. The I/Osubsystem 111 includes an I/O hub 107 that can enable the computingsystem 100 to receive input from one or more input device(s) 108.Additionally, the I/O hub 107 can enable a display controller, which maybe included in the one or more processor(s) 102, to provide outputs toone or more display device(s) 110A. In one embodiment the one or moredisplay device(s) 110A coupled with the I/O hub 107 can include a local,internal, or embedded display device.

In one embodiment the processing subsystem 101 includes one or moreparallel processor(s) 112 coupled to memory hub 105 via a bus or othercommunication link 113. The communication link 113 may be one of anynumber of standards based communication link technologies or protocols,such as, but not limited to PCI Express, or may be a vendor specificcommunications interface or communications fabric. In one embodiment theone or more parallel processor(s) 112 form a computationally focusedparallel or vector processing system that can include a large number ofprocessing cores and/or processing clusters, such as a many integratedcore (MIC) processor. In one embodiment the one or more parallelprocessor(s) 112 form a graphics processing subsystem that can outputpixels to one of the one or more display device(s) 110A coupled via theI/O Hub 107. The one or more parallel processor(s) 112 can also includea display controller and display interface (not shown) to enable adirect connection to one or more display device(s) 110B.

Within the I/O subsystem 111, a system storage unit 114 can connect tothe I/O hub 107 to provide a storage mechanism for the computing system100. An I/O switch 116 can be used to provide an interface mechanism toenable connections between the I/O hub 107 and other components, such asa network adapter 118 and/or wireless network adapter 119 that may beintegrated into the platform, and various other devices that can beadded via one or more add-in device(s) 120. The network adapter 118 canbe an Ethernet adapter or another wired network adapter. The wirelessnetwork adapter 119 can include one or more of a Wi-Fi, Bluetooth, nearfield communication (NFC), or other network device that includes one ormore wireless radios.

The computing system 100 can include other components not explicitlyshown, including USB or other port connections, optical storage drives,video capture devices, and the like, may also be connected to the I/Ohub 107. Communication paths interconnecting the various components inFIG. 1 may be implemented using any suitable protocols, such as PCI(Peripheral Component Interconnect) based protocols (e.g., PCI-Express),or any other bus or point-to-point communication interfaces and/orprotocol(s), such as the NV-Link high-speed interconnect, orinterconnect protocols known in the art.

In one embodiment, the one or more parallel processor(s) 112 incorporatecircuitry optimized for graphics and video processing, including, forexample, video output circuitry, and constitutes a graphics processingunit (GPU). In another embodiment, the one or more parallel processor(s)112 incorporate circuitry optimized for general purpose processing,while preserving the underlying computational architecture, described ingreater detail herein. In yet another embodiment, components of thecomputing system 100 may be integrated with one or more other systemelements on a single integrated circuit. For example, the one or moreparallel processor(s) 112, memory hub 105, processor(s) 102, and I/O hub107 can be integrated into a system on chip (SoC) integrated circuit.Alternatively, the components of the computing system 100 can beintegrated into a single package to form a system in package (SIP)configuration. In one embodiment at least a portion of the components ofthe computing system 100 can be integrated into a multi-chip module(MCM), which can be interconnected with other multi-chip modules into amodular computing system.

It will be appreciated that the computing system 100 shown herein isillustrative and that variations and modifications are possible. Theconnection topology, including the number and arrangement of bridges,the number of processor(s) 102, and the number of parallel processor(s)112, may be modified as desired. For instance, in some embodiments,system memory 104 is connected to the processor(s) 102 directly ratherthan through a bridge, while other devices communicate with systemmemory 104 via the memory hub 105 and the processor(s) 102. In otheralternative topologies, the parallel processor(s) 112 are connected tothe I/O hub 107 or directly to one of the one or more processor(s) 102,rather than to the memory hub 105. In other embodiments, the I/O hub 107and memory hub 105 may be integrated into a single chip. Someembodiments may include two or more sets of processor(s) 102 attachedvia multiple sockets, which can couple with two or more instances of theparallel processor(s) 112.

Some of the particular components shown herein are optional and may notbe included in all implementations of the computing system 100. Forexample, any number of add-in cards or peripherals may be supported, orsome components may be eliminated. Furthermore, some architectures mayuse different terminology for components similar to those illustrated inFIG. 1. For example, the memory hub 105 may be referred to as aNorthbridge in some architectures, while the I/O hub 107 may be referredto as a Southbridge.

FIG. 2A illustrates a parallel processor 200, according to anembodiment. The various components of the parallel processor 200 may beimplemented using one or more integrated circuit devices, such asprogrammable processors, application specific integrated circuits(ASICs), or field programmable gate arrays (FPGA). The illustratedparallel processor 200 is a variant of the one or more parallelprocessor(s) 112 shown in FIG. 1, according to an embodiment.

In one embodiment the parallel processor 200 includes a parallelprocessing unit 202. The parallel processing unit includes an I/O unit204 that enables communication with other devices, including otherinstances of the parallel processing unit 202. The I/O unit 204 may bedirectly connected to other devices. In one embodiment the I/O unit 204connects with other devices via the use of a hub or switch interface,such as memory hub 105. The connections between the memory hub 105 andthe I/O unit 204 form a communication link 113. Within the parallelprocessing unit 202, the I/O unit 204 connects with a host interface 206and a memory crossbar 216, where the host interface 206 receivescommands directed to performing processing operations and the memorycrossbar 216 receives commands directed to performing memory operations.

When the host interface 206 receives a command buffer via the I/O unit204, the host interface 206 can direct work operations to perform thosecommands to a front end 208. In one embodiment the front end 208 coupleswith a scheduler 210, which is configured to distribute commands orother work items to a processing cluster array 212. In one embodimentthe scheduler 210 ensures that the processing cluster array 212 isproperly configured and in a valid state before tasks are distributed tothe processing clusters of the processing cluster array 212. In oneembodiment the scheduler 210 is implemented via firmware logic executingon a microcontroller. The microcontroller implemented scheduler 210 isconfigurable to perform complex scheduling and work distributionoperations at coarse and fine granularity, enabling rapid preemption andcontext switching of threads executing on the processing array 212. Inone embodiment, the host software can prove workloads for scheduling onthe processing array 212 via one of multiple graphics processingdoorbells. The workloads can then be automatically distributed acrossthe processing array 212 by the scheduler 210 logic within the schedulermicrocontroller.

The processing cluster array 212 can include up to “N” processingclusters (e.g., cluster 214A, cluster 214B, through cluster 214N). Eachcluster 214A-214N of the processing cluster array 212 can execute alarge number of concurrent threads. The scheduler 210 can allocate workto the clusters 214A-214N of the processing cluster array 212 usingvarious scheduling and/or work distribution algorithms, which may varydepending on the workload arising for each type of program orcomputation. The scheduling can be handled dynamically by the scheduler210, or can be assisted in part by compiler logic during compilation ofprogram logic configured for execution by the processing cluster array212. In one embodiment, different processing clusters 214A-214N of theprocessing cluster array 212 can be allocated for processing differenttypes of programs or for performing different types of computations.

The processing cluster array 212 can be configured to perform varioustypes of parallel processing operations. In one embodiment theprocessing cluster array 212 is configured to perform general-purposeparallel compute operations. For example, the processing cluster array212 can include logic to execute processing tasks including filtering ofvideo and/or audio data, performing modeling operations, includingphysics operations, and performing data transformations.

In one embodiment the processing cluster array 212 is configured toperform parallel graphics processing operations. In embodiments in whichthe parallel processor 200 is configured to perform graphics processingoperations, the processing cluster array 212 can include additionallogic to support the execution of such graphics processing operations,including, but not limited to texture sampling logic to perform textureoperations, as well as tessellation logic and other vertex processinglogic. Additionally, the processing cluster array 212 can be configuredto execute graphics processing related shader programs such as, but notlimited to vertex shaders, tessellation shaders, geometry shaders, andpixel shaders. The parallel processing unit 202 can transfer data fromsystem memory via the I/O unit 204 for processing. During processing thetransferred data can be stored to on-chip memory (e.g., parallelprocessor memory 222) during processing, then written back to systemmemory.

In one embodiment, when the parallel processing unit 202 is used toperform graphics processing, the scheduler 210 can be configured todivide the processing workload into approximately equal sized tasks, tobetter enable distribution of the graphics processing operations tomultiple processing clusters 214A-214N of the processing cluster array212. In some embodiments, portions of the processing cluster array 212can be configured to perform different types of processing. For examplea first portion may be configured to perform vertex shading and topologygeneration, a second portion may be configured to perform tessellationand geometry shading, and a third portion may be configured to performpixel shading or other screen space operations, to produce a renderedimage for display. Intermediate data produced by one or more of theprocessing clusters 214A-214N may be stored in buffers to allow theintermediate data to be transmitted between the processing clusters214A-214N for further processing.

During operation, the processing cluster array 212 can receiveprocessing tasks to be executed via the scheduler 210, which receivescommands defining processing tasks from front end 208. For graphicsprocessing operations, processing tasks can include indices of data tobe processed, e.g., surface (patch) data, primitive data, vertex data,and/or pixel data, as well as state parameters and commands defining howthe data is to be processed (e.g., what program is to be executed). Thescheduler 210 may be configured to fetch the indices corresponding tothe tasks or may receive the indices from the front end 208. The frontend 208 can be configured to ensure the processing cluster array 212 isconfigured to a valid state before the workload specified by incomingcommand buffers (e.g., batch-buffers, push buffers, etc.) is initiated.

Each of the one or more instances of the parallel processing unit 202can couple with parallel processor memory 222. The parallel processormemory 222 can be accessed via the memory crossbar 216, which canreceive memory requests from the processing cluster array 212 as well asthe I/O unit 204. The memory crossbar 216 can access the parallelprocessor memory 222 via a memory interface 218. The memory interface218 can include multiple partition units (e.g., partition unit 220A,partition unit 220B, through partition unit 220N) that can each coupleto a portion (e.g., memory unit) of parallel processor memory 222. Inone implementation the number of partition units 220A-220N is configuredto be equal to the number of memory units, such that a first partitionunit 220A has a corresponding first memory unit 224A, a second partitionunit 220B has a corresponding memory unit 224B, and an Nth partitionunit 220N has a corresponding Nth memory unit 224N. In otherembodiments, the number of partition units 220A-220N may not be equal tothe number of memory devices.

In various embodiments, the memory units 224A-224N can include varioustypes of memory devices, including dynamic random access memory (DRAM)or graphics random access memory, such as synchronous graphics randomaccess memory (SGRAM), including graphics double data rate (GDDR)memory. In one embodiment, the memory units 224A-224N may also include3D stacked memory, including but not limited to high bandwidth memory(HBM). Persons skilled in the art will appreciate that the specificimplementation of the memory units 224A-224N can vary, and can beselected from one of various conventional designs. Render targets, suchas frame buffers or texture maps may be stored across the memory units224A-224N, allowing partition units 220A-220N to write portions of eachrender target in parallel to efficiently use the available bandwidth ofparallel processor memory 222. In some embodiments, a local instance ofthe parallel processor memory 222 may be excluded in favor of a unifiedmemory design that utilizes system memory in conjunction with localcache memory.

In one embodiment, any one of the processing clusters 214A-214N of theprocessing cluster array 212 can process data that will be written toany of the memory units 224A-224N within parallel processor memory 222.The memory crossbar 216 can be configured to transfer the output of eachprocessing cluster 214A-214N to any partition unit 220A-220N or toanother processing cluster 214A-214N, which can perform additionalprocessing operations on the output. Each processing cluster 214A-214Ncan communicate with the memory interface 218 through the memorycrossbar 216 to read from or write to various external memory devices.In one embodiment the memory crossbar 216 has a connection to the memoryinterface 218 to communicate with the I/O unit 204, as well as aconnection to a local instance of the parallel processor memory 222,enabling the processing units within the different processing clusters214A-214N to communicate with system memory or other memory that is notlocal to the parallel processing unit 202. In one embodiment the memorycrossbar 216 can use virtual channels to separate traffic streamsbetween the processing clusters 214A-214N and the partition units220A-220N.

While a single instance of the parallel processing unit 202 isillustrated within the parallel processor 200, any number of instancesof the parallel processing unit 202 can be included. For example,multiple instances of the parallel processing unit 202 can be providedon a single add-in card, or multiple add-in cards can be interconnected.The different instances of the parallel processing unit 202 can beconfigured to inter-operate even if the different instances havedifferent numbers of processing cores, different amounts of localparallel processor memory, and/or other configuration differences. Forexample, in one embodiment some instances of the parallel processingunit 202 can include higher precision floating point units relative toother instances. Systems incorporating one or more instances of theparallel processing unit 202 or the parallel processor 200 can beimplemented in a variety of configurations and form factors, includingbut not limited to desktop, laptop, or handheld personal computers,servers, workstations, game consoles, and/or embedded systems.

FIG. 2B is a block diagram of a partition unit 220, according to anembodiment. In one embodiment the partition unit 220 is an instance ofone of the partition units 220A-220N of FIG. 2A. As illustrated, thepartition unit 220 includes an L2 cache 221, a frame buffer interface225, and a ROP 226 (raster operations unit). The L2 cache 221 is aread/write cache that is configured to perform load and store operationsreceived from the memory crossbar 216 and ROP 226. Read misses andurgent write-back requests are output by L2 cache 221 to frame bufferinterface 225 for processing. Updates can also be sent to the framebuffer via the frame buffer interface 225 for processing. In oneembodiment the frame buffer interface 225 interfaces with one of thememory units in parallel processor memory, such as the memory units224A-224N of FIG. 2A (e.g., within parallel processor memory 222).

In graphics applications, the ROP 226 is a processing unit that performsraster operations such as stencil, z test, blending, and the like. TheROP 226 then outputs processed graphics data that is stored in graphicsmemory. In some embodiments the ROP 226 includes compression logic tocompress depth or color data that is written to memory and decompressdepth or color data that is read from memory. The compression logic canbe lossless compression logic that makes use of one or more of multiplecompression algorithms. The type of compression that is performed by theROP 226 can vary based on the statistical characteristics of the data tobe compressed. For example, in one embodiment, delta color compressionis performed on depth and color data on a per-tile basis.

In some embodiments, the ROP 226 is included within each processingcluster (e.g., processing cluster 214A-214N of FIG. 2A) instead ofwithin the partition unit 220. In such embodiment, read and writerequests for pixel data are transmitted over the memory crossbar 216instead of pixel fragment data. The processed graphics data may bedisplayed on a display device, such as one of the one or more displaydevice(s) 110 of FIG. 1, routed for further processing by theprocessor(s) 102, or routed for further processing by one of theprocessing entities within the parallel processor 200 of FIG. 2A.

FIG. 2C is a block diagram of a processing cluster 214 within a parallelprocessing unit, according to an embodiment. In one embodiment theprocessing cluster is an instance of one of the processing clusters214A-214N of FIG. 2A. The processing cluster 214 can be configured toexecute many threads in parallel, where the term “thread” refers to aninstance of a particular program executing on a particular set of inputdata. In some embodiments, single-instruction, multiple-data (SIMD)instruction issue techniques are used to support parallel execution of alarge number of threads without providing multiple independentinstruction units. In other embodiments, single-instruction,multiple-thread (SIMT) techniques are used to support parallel executionof a large number of generally synchronized threads, using a commoninstruction unit configured to issue instructions to a set of processingengines within each one of the processing clusters. Unlike a SIMDexecution regime, where all processing engines typically executeidentical instructions, SIMT execution allows different threads to morereadily follow divergent execution paths through a given thread program.Persons skilled in the art will understand that a SIMD processing regimerepresents a functional subset of a SIMT processing regime.

Operation of the processing cluster 214 can be controlled via a pipelinemanager 232 that distributes processing tasks to SIMT parallelprocessors. The pipeline manager 232 receives instructions from thescheduler 210 of FIG. 2A and manages execution of those instructions viaa graphics multiprocessor 234 and/or a texture unit 236. The illustratedgraphics multiprocessor 234 is an exemplary instance of a SIMT parallelprocessor. However, various types of SIMT parallel processors ofdiffering architectures may be included within the processing cluster214. One or more instances of the graphics multiprocessor 234 can beincluded within a processing cluster 214. The graphics multiprocessor234 can process data and a data crossbar 240 can be used to distributethe processed data to one of multiple possible destinations, includingother shader units. The pipeline manager 232 can facilitate thedistribution of processed data by specifying destinations for processeddata to be distributed via the data crossbar 240.

Each graphics multiprocessor 234 within the processing cluster 214 caninclude an identical set of functional execution logic (e.g., arithmeticlogic units, load-store units, etc.). The functional execution logic canbe configured in a pipelined manner in which new instructions can beissued before previous instructions are complete. The functionalexecution logic supports a variety of operations including integer andfloating point arithmetic, comparison operations, Boolean operations,bit-shifting, and computation of various algebraic functions. In oneembodiment the same functional-unit hardware can be leveraged to performdifferent operations and any combination of functional units may bepresent.

The instructions transmitted to the processing cluster 214 constitutes athread. A set of threads executing across the set of parallel processingengines is a thread group. A thread group executes the same program ondifferent input data. Each thread within a thread group can be assignedto a different processing engine within a graphics multiprocessor 234. Athread group may include fewer threads than the number of processingengines within the graphics multiprocessor 234. When a thread groupincludes fewer threads than the number of processing engines, one ormore of the processing engines may be idle during cycles in which thatthread group is being processed. A thread group may also include morethreads than the number of processing engines within the graphicsmultiprocessor 234. When the thread group includes more threads than thenumber of processing engines within the graphics multiprocessor 234,processing can be performed over consecutive clock cycles. In oneembodiment multiple thread groups can be executed concurrently on agraphics multiprocessor 234.

In one embodiment the graphics multiprocessor 234 includes an internalcache memory to perform load and store operations. In one embodiment,the graphics multiprocessor 234 can forego an internal cache and use acache memory (e.g., L1 cache 248) within the processing cluster 214.Each graphics multiprocessor 234 also has access to L2 caches within thepartition units (e.g., partition units 220A-220N of FIG. 2A) that areshared among all processing clusters 214 and may be used to transferdata between threads. The graphics multiprocessor 234 may also accessoff-chip global memory, which can include one or more of local parallelprocessor memory and/or system memory. Any memory external to theparallel processing unit 202 may be used as global memory. Embodimentsin which the processing cluster 214 includes multiple instances of thegraphics multiprocessor 234 can share common instructions and data,which may be stored in the L1 cache 248.

Each processing cluster 214 may include an MMU 245 (memory managementunit) that is configured to map virtual addresses into physicaladdresses. In other embodiments, one or more instances of the MMU 245may reside within the memory interface 218 of FIG. 2A. The MMU 245includes a set of page table entries (PTEs) used to map a virtualaddress to a physical address of a tile and optionally a cache lineindex. The MMU 245 may include address translation lookaside buffers(TLB) or caches that may reside within the graphics multiprocessor 234or the L1 cache or processing cluster 214. The physical address isprocessed to distribute surface data access locality to allow efficientrequest interleaving among partition units. The cache line index may beused to determine whether a request for a cache line is a hit or miss.

In graphics and computing applications, a processing cluster 214 may beconfigured such that each graphics multiprocessor 234 is coupled to atexture unit 236 for performing texture mapping operations, e.g.,determining texture sample positions, reading texture data, andfiltering the texture data. Texture data is read from an internaltexture L1 cache (not shown) or in some embodiments from the L1 cachewithin graphics multiprocessor 234 and is fetched from an L2 cache,local parallel processor memory, or system memory, as needed. Eachgraphics multiprocessor 234 outputs processed tasks to the data crossbar240 to provide the processed task to another processing cluster 214 forfurther processing or to store the processed task in an L2 cache, localparallel processor memory, or system memory via the memory crossbar 216.A preROP 242 (pre-raster operations unit) is configured to receive datafrom graphics multiprocessor 234, direct data to ROP units, which may belocated with partition units as described herein (e.g., partition units220A-220N of FIG. 2A). The preROP 242 unit can perform optimizations forcolor blending, organize pixel color data, and perform addresstranslations.

It will be appreciated that the core architecture described herein isillustrative and that variations and modifications are possible. Anynumber of processing units, e.g., graphics multiprocessor 234, textureunits 236, preROPs 242, etc., may be included within a processingcluster 214. Further, while only one processing cluster 214 is shown, aparallel processing unit as described herein may include any number ofinstances of the processing cluster 214. In one embodiment, eachprocessing cluster 214 can be configured to operate independently ofother processing clusters 214 using separate and distinct processingunits, L1 caches, etc.

FIG. 2D shows a graphics multiprocessor 234, according to oneembodiment. In such embodiment the graphics multiprocessor 234 coupleswith the pipeline manager 232 of the processing cluster 214. Thegraphics multiprocessor 234 has an execution pipeline including but notlimited to an instruction cache 252, an instruction unit 254, an addressmapping unit 256, a register file 258, one or more general purposegraphics processing unit (GPGPU) cores 262, and one or more load/storeunits 266. The GPGPU cores 262 and load/store units 266 are coupled withcache memory 272 and shared memory 270 via a memory and cacheinterconnect 268.

In one embodiment, the instruction cache 252 receives a stream ofinstructions to execute from the pipeline manager 232. The instructionsare cached in the instruction cache 252 and dispatched for execution bythe instruction unit 254. The instruction unit 254 can dispatchinstructions as thread groups (e.g., warps), with each thread of thethread group assigned to a different execution unit within GPGPU core262. An instruction can access any of a local, shared, or global addressspace by specifying an address within a unified address space. Theaddress mapping unit 256 can be used to translate addresses in theunified address space into a distinct memory address that can beaccessed by the load/store units 266.

The register file 258 provides a set of registers for the functionalunits of the graphics multiprocessor 234. The register file 258 providestemporary storage for operands connected to the data paths of thefunctional units (e.g., GPGPU cores 262, load/store units 266) of thegraphics multiprocessor 234. In one embodiment, the register file 258 isdivided between each of the functional units such that each functionalunit is allocated a dedicated portion of the register file 258. In oneembodiment, the register file 258 is divided between the different warpsbeing executed by the graphics multiprocessor 234.

The GPGPU cores 262 can each include floating point units (FPUs) and/orinteger arithmetic logic units (ALUs) that are used to executeinstructions of the graphics multiprocessor 234. The GPGPU cores 262 canbe similar in architecture or can differ in architecture, according toembodiments. For example and in one embodiment, a first portion of theGPGPU cores 262 include a single precision FPU and an integer ALU whilea second portion of the GPGPU cores include a double precision FPU. Inone embodiment the FPUs can implement the IEEE 754-2008 standard forfloating point arithmetic or enable variable precision floating pointarithmetic. The graphics multiprocessor 234 can additionally include oneor more fixed function or special function units to perform specificfunctions such as copy rectangle or pixel blending operations. In oneembodiment one or more of the GPGPU cores can also include fixed orspecial function logic.

In one embodiment the GPGPU cores 262 include SIMD logic capable ofperforming a single instruction on multiple sets of data. In oneembodiment GPGPU cores 262 can physically execute SIMD4, SIMD8, andSIMD16 instructions and logically execute SIMD1, SIMD2, and SIMD32instructions. The SIMD instructions for the GPGPU cores can be generatedat compile time by a shader compiler or automatically generated whenexecuting programs written and compiled for single program multiple data(SPMD) or SIMT architectures. Multiple threads of a program configuredfor the SIMT execution model can be executed via a single SIMDinstruction. For example and in one embodiment, eight SIMT threads thatperform the same or similar operations can be executed in parallel via asingle SIMD8 logic unit.

The memory and cache interconnect 268 is an interconnect network thatconnects each of the functional units of the graphics multiprocessor 234to the register file 258 and to the shared memory 270. In oneembodiment, the memory and cache interconnect 268 is a crossbarinterconnect that allows the load/store unit 266 to implement load andstore operations between the shared memory 270 and the register file258. The register file 258 can operate at the same frequency as theGPGPU cores 262, thus data transfer between the GPGPU cores 262 and theregister file 258 is very low latency. The shared memory 270 can be usedto enable communication between threads that execute on the functionalunits within the graphics multiprocessor 234. The cache memory 272 canbe used as a data cache for example, to cache texture data communicatedbetween the functional units and the texture unit 236. The shared memory270 can also be used as a program managed cached. Threads executing onthe GPGPU cores 262 can programmatically store data within the sharedmemory in addition to the automatically cached data that is storedwithin the cache memory 272.

FIG. 3A-3B illustrate additional graphics multiprocessors, according toembodiments. The illustrated graphics multiprocessors 325, 350 arevariants of the graphics multiprocessor 234 of FIG. 2C. The illustratedgraphics multiprocessors 325, 350 can be configured as a streamingmultiprocessor (SM) capable of simultaneous execution of a large numberof execution threads.

FIG. 3A shows a graphics multiprocessor 325 according to an additionalembodiment. The graphics multiprocessor 325 includes multiple additionalinstances of execution resource units relative to the graphicsmultiprocessor 234 of FIG. 2D. For example, the graphics multiprocessor325 can include multiple instances of the instruction unit 332A-332B,register file 334A-334B, and texture unit(s) 344A-344B. The graphicsmultiprocessor 325 also includes multiple sets of graphics or computeexecution units (e.g., GPGPU core 336A-336B, GPGPU core 337A-337B, GPGPUcore 338A-338B) and multiple sets of load/store units 340A-340B. In oneembodiment the execution resource units have a common instruction cache330, texture and/or data cache memory 342, and shared memory 346.

The various components can communicate via an interconnect fabric 327.In one embodiment the interconnect fabric 327 includes one or morecrossbar switches to enable communication between the various componentsof the graphics multiprocessor 325. In one embodiment the interconnectfabric 327 is a separate, high-speed network fabric layer upon whicheach component of the graphics multiprocessor 325 is stacked. Thecomponents of the graphics multiprocessor 325 communicate with remotecomponents via the interconnect fabric 327. For example, the GPGPU cores336A-336B, 337A-337B, and 3378A-338B can each communicate with sharedmemory 346 via the interconnect fabric 327. The interconnect fabric 327can arbitrate communication within the graphics multiprocessor 325 toensure a fair bandwidth allocation between components.

FIG. 3B shows a graphics multiprocessor 350 according to an additionalembodiment. The graphics processor includes multiple sets of executionresources 356A-356D, where each set of execution resource includesmultiple instruction units, register files, GPGPU cores, and load storeunits, as illustrated in FIG. 2D and FIG. 3A. The execution resources356A-356D can work in concert with texture unit(s) 360A-360D for textureoperations, while sharing an instruction cache 354, and shared memory362. In one embodiment the execution resources 356A-356D can share aninstruction cache 354 and shared memory 362, as well as multipleinstances of a texture and/or data cache memory 358A-358B. The variouscomponents can communicate via an interconnect fabric 352 similar to theinterconnect fabric 327 of FIG. 3A.

Persons skilled in the art will understand that the architecturedescribed in FIGS. 1, 2A-2D, and 3A-3B are descriptive and not limitingas to the scope of the present embodiments. Thus, the techniquesdescribed herein may be implemented on any properly configuredprocessing unit, including, without limitation, one or more mobileapplication processors, one or more desktop or server central processingunits (CPUs) including multi-core CPUs, one or more parallel processingunits, such as the parallel processing unit 202 of FIG. 2A, as well asone or more graphics processors or special purpose processing units,without departure from the scope of the embodiments described herein.

In some embodiments a parallel processor or GPGPU as described herein iscommunicatively coupled to host/processor cores to accelerate graphicsoperations, machine-learning operations, pattern analysis operations,and various general purpose GPU (GPGPU) functions. The GPU may becommunicatively coupled to the host processor/cores over a bus or otherinterconnect (e.g., a high speed interconnect such as PCIe or NVLink).In other embodiments, the GPU may be integrated on the same package orchip as the cores and communicatively coupled to the cores over aninternal processor bus/interconnect (i.e., internal to the package orchip). Regardless of the manner in which the GPU is connected, theprocessor cores may allocate work to the GPU in the form of sequences ofcommands/instructions contained in a work descriptor. The GPU then usesdedicated circuitry/logic for efficiently processing thesecommands/instructions.

Techniques for GPU to Host Processor Interconnection

FIG. 4A illustrates an exemplary architecture in which a plurality ofGPUs 410-413 are communicatively coupled to a plurality of multi-coreprocessors 405-406 over high-speed links 440A-440D (e.g., buses,point-to-point interconnects, etc.). In one embodiment, the high-speedlinks 440A-440D support a communication throughput of 4 GB/s, 30 GB/s,80 GB/s or higher, depending on the implementation. Various interconnectprotocols may be used including, but not limited to, PCIe 4.0 or 5.0 andNVLink 2.0. However, the underlying principles of the invention are notlimited to any particular communication protocol or throughput.

In addition, in one embodiment, two or more of the GPUs 410-413 areinterconnected over high-speed links 442A-442B, which may be implementedusing the same or different protocols/links than those used forhigh-speed links 440A-440D. Similarly, two or more of the multi-coreprocessors 405-406 may be connected over high speed link 443 which maybe symmetric multi-processor (SMP) buses operating at 20 GB/s, 30 GB/s,120 GB/s or higher. Alternatively, all communication between the varioussystem components shown in FIG. 4A may be accomplished using the sameprotocols/links (e.g., over a common interconnection fabric). Asmentioned, however, the underlying principles of the invention are notlimited to any particular type of interconnect technology.

In one embodiment, each multi-core processor 405-406 is communicativelycoupled to a processor memory 401-402, via memory interconnects430A-430B, respectively, and each GPU 410-413 is communicatively coupledto GPU memory 420-423 over GPU memory interconnects 450A-450D,respectively. The memory interconnects 430A-430B and 450A-450D mayutilize the same or different memory access technologies. By way ofexample, and not limitation, the processor memories 401-402 and GPUmemories 420-423 may be volatile memories such as dynamic random accessmemories (DRAMs) (including stacked DRAMs), Graphics DDR SDRAM (GDDR)(e.g., GDDR5, GDDR6), or High Bandwidth Memory (HBM) and/or may benon-volatile memories such as 3D XPoint or Nano-Ram. In one embodiment,some portion of the memories may be volatile memory and another portionmay be non-volatile memory (e.g., using a two-level memory (2LM)hierarchy).

As described below, although the various processors 405-406 and GPUs410-413 may be physically coupled to a particular memory 401-402,420-423, respectively, a unified memory architecture may be implementedin which the same virtual system address space (also referred to as the“effective address” space) is distributed among all of the variousphysical memories. For example, processor memories 401-402 may eachcomprise 64 GB of the system memory address space and GPU memories420-423 may each comprise 32 GB of the system memory address space(resulting in a total of 256 GB addressable memory in this example).

FIG. 4B illustrates additional details for an interconnection between amulti-core processor 407 and a graphics acceleration module 446 inaccordance with one embodiment. The graphics acceleration module 446 mayinclude one or more GPU chips integrated on a line card which is coupledto the processor 407 via the high-speed link 440. Alternatively, thegraphics acceleration module 446 may be integrated on the same packageor chip as the processor 407.

The illustrated processor 407 includes a plurality of cores 460A-460D,each with a translation lookaside buffer 461A-461D and one or morecaches 462A-462D. The cores may include various other components forexecuting instructions and processing data which are not illustrated toavoid obscuring the underlying principles of the invention (e.g.,instruction fetch units, branch prediction units, decoders, executionunits, reorder buffers, etc.). The caches 462A-462D may comprise level 1(L1) and level 2 (L2) caches. In addition, one or more shared caches 456may be included in the caching hierarchy and shared by sets of the cores460A-460D. For example, one embodiment of the processor 407 includes 24cores, each with its own L1 cache, twelve shared L2 caches, and twelveshared L3 caches. In this embodiment, one of the L2 and L3 caches areshared by two adjacent cores. The processor 407 and the graphicsaccelerator integration module 446 connect with system memory 441, whichmay include processor memories 401-402.

Coherency is maintained for data and instructions stored in the variouscaches 462A-462D, 456 and system memory 441 via inter-core communicationover a coherence bus 464. For example, each cache may have cachecoherency logic/circuitry associated therewith to communicate to overthe coherence bus 464 in response to detected reads or writes toparticular cache lines. In one implementation, a cache snooping protocolis implemented over the coherence bus 464 to snoop cache accesses. Cachesnooping/coherency techniques are well understood by those of skill inthe art and will not be described in detail here to avoid obscuring theunderlying principles of the invention.

In one embodiment, a proxy circuit 425 communicatively couples thegraphics acceleration module 446 to the coherence bus 464, allowing thegraphics acceleration module 446 to participate in the cache coherenceprotocol as a peer of the cores. In particular, an interface 435provides connectivity to the proxy circuit 425 over high-speed link 440(e.g., a PCIe bus, NVLink, etc.) and an interface 437 connects thegraphics acceleration module 446 to the high-speed link 440.

In one implementation, an accelerator integration circuit 436 providescache management, memory access, context management, and interruptmanagement services on behalf of a plurality of graphics processingengines 431, 432, N of the graphics acceleration module 446. Thegraphics processing engines 431, 432, N may each comprise a separategraphics processing unit (GPU). Alternatively, the graphics processingengines 431, 432, N may comprise different types of graphics processingengines within a GPU such as graphics execution units, media processingengines (e.g., video encoders/decoders), samplers, and blit engines. Inother words, the graphics acceleration module may be a GPU with aplurality of graphics processing engines 431-432, N or the graphicsprocessing engines 431-432, N may be individual GPUs integrated on acommon package, line card, or chip.

In one embodiment, the accelerator integration circuit 436 includes amemory management unit (MMU) 439 for performing various memorymanagement functions such as virtual-to-physical memory translations(also referred to as effective-to-real memory translations) and memoryaccess protocols for accessing system memory 441. The MMU 439 may alsoinclude a translation lookaside buffer (TLB) (not shown) for caching thevirtual/effective to physical/real address translations. In oneembodiment, the accelerator integration circuit 436 includes a fetchunit 491 to fetch commands, instructions, work descriptors, etc., thatdefine operations to be performed. In one implementation, a cache 438stores commands and data for efficient access by the graphics processingengines 431-432, N. In one embodiment, the data stored in cache 438 andgraphics memories 433-434, M is kept coherent with the core caches462A-462D, 456 and system memory 411. As mentioned, this may beaccomplished via proxy circuit 425 which takes part in the cachecoherency mechanism on behalf of cache 438 and memories 433-434, M(e.g., sending updates to the cache 438 related tomodifications/accesses of cache lines on processor caches 462A-462D, 456and receiving updates from the cache 438).

A set of registers 445 store context data for threads executed by thegraphics processing engines 431-432, N and a context management circuit448 manages the thread contexts. For example, the context managementcircuit 448 may perform save and restore operations to save and restorecontexts of the various threads during contexts switches (e.g., where afirst thread is saved and a second thread is stored so that the secondthread can be execute by a graphics processing engine). For example, ona context switch, the context management circuit 448 may store currentregister values to a designated region in memory (e.g., identified by acontext pointer). It may then restore the register values when returningto the context. In one embodiment, an interrupt management circuit 447receives and processes interrupts received from system devices.

In one implementation, virtual/effective addresses from a graphicsprocessing engine 431 are translated to real/physical addresses insystem memory 411 by the MMU 439. One embodiment of the acceleratorintegration circuit 436 supports multiple (e.g., 4, 8, 16) graphicsaccelerator modules 446 and/or other accelerator devices. The graphicsaccelerator module 446 may be dedicated to a single application executedon the processor 407 or may be shared between multiple applications. Inone embodiment, a virtualized graphics execution environment ispresented in which the resources of the graphics processing engines431-432, N are shared with multiple applications or virtual machines(VMs). The resources may be subdivided into “slices” which are allocatedto different VMs and/or applications based on the processingrequirements and priorities associated with the VMs and/or applications.

Thus, the accelerator integration circuit acts as a bridge to the systemfor the graphics acceleration module 446 and provides addresstranslation and system memory cache services. In addition, theaccelerator integration circuit 436 may provide virtualizationfacilities for the host processor to manage virtualization of thegraphics processing engines, interrupts, and memory management.

Because hardware resources of the graphics processing engines 431-432, Nare mapped explicitly to the real address space seen by the hostprocessor 407, any host processor can address these resources directlyusing an effective address value. One function of the acceleratorintegration circuit 436, in one embodiment, is the physical separationof the graphics processing engines 431-432, N so that they appear to thesystem as independent units.

As mentioned, in the illustrated embodiment, one or more graphicsmemories 433-434, M are coupled to each of the graphics processingengines 431-432, N, respectively. The graphics memories 433-434, M storeinstructions and data being processed by each of the graphics processingengines 431-432, N. The graphics memories 433-434, M may be volatilememories such as DRAMs (including stacked DRAMs), GDDR memory (e.g.,GDDR5, GDDR6), or HBM, and/or may be non-volatile memories such as 3DXPoint or Nano-Ram.

In one embodiment, to reduce data traffic over the high-speed link 440,biasing techniques are used to ensure that the data stored in graphicsmemories 433-434, M is data which will be used most frequently by thegraphics processing engines 431-432, N and preferably not used by thecores 460A-460D (at least not frequently). Similarly, the biasingmechanism attempts to keep data needed by the cores (and preferably notthe graphics processing engines 431-432, N) within the caches 462A-462D,456 of the cores and system memory 411.

FIG. 4C illustrates another embodiment in which the acceleratorintegration circuit 436 is integrated within the processor 407. In thisembodiment, the graphics processing engines 431-432, N communicatedirectly over the high-speed link 440 to the accelerator integrationcircuit 436 via interface 437 and interface 435 (which, again, may beutilize any form of bus or interface protocol). The acceleratorintegration circuit 436 may perform the same operations as thosedescribed with respect to FIG. 4B, but potentially at a higherthroughput given its close proximity to the coherency bus 464 and caches462A-462D, 456.

One embodiment supports different programming models including adedicated-process programming model (no graphics acceleration modulevirtualization) and shared programming models (with virtualization). Thelatter may include programming models which are controlled by theaccelerator integration circuit 436 and programming models which arecontrolled by the graphics acceleration module 446.

In one embodiment of the dedicated process model, graphics processingengines 431-432, N are dedicated to a single application or processunder a single operating system. The single application can funnel otherapplication requests to the graphics engines 431-432, N, providingvirtualization within a VM/partition.

In the dedicated-process programming models, the graphics processingengines 431-432, N, may be shared by multiple VM/application partitions.The shared models require a system hypervisor to virtualize the graphicsprocessing engines 431-432, N to allow access by each operating system.For single-partition systems without a hypervisor, the graphicsprocessing engines 431-432, N are owned by the operating system. In bothcases, the operating system can virtualize the graphics processingengines 431-432, N to provide access to each process or application.

For the shared programming model, the graphics acceleration module 446or an individual graphics processing engine 431-432, N selects a processelement using a process handle. In one embodiment, process elements arestored in system memory 411 and are addressable using the effectiveaddress to real address translation techniques described herein. Theprocess handle may be an implementation-specific value provided to thehost process when registering its context with the graphics processingengine 431-432, N (that is, calling system software to add the processelement to the process element linked list). The lower 16-bits of theprocess handle may be the offset of the process element within theprocess element linked list.

FIG. 4D illustrates an exemplary accelerator integration slice 490. Asused herein, a “slice” comprises a specified portion of the processingresources of the accelerator integration circuit 436. Applicationeffective address space 482 within system memory 411 stores processelements 483. In one embodiment, the process elements 483 are stored inresponse to GPU invocations 481 from applications 480 executed on theprocessor 407. A process element 483 contains the process state for thecorresponding application 480. A work descriptor (WD) 484 contained inthe process element 483 can be a single job requested by an applicationor may contain a pointer to a queue of jobs. In the latter case, the WD484 is a pointer to the job request queue in the application's addressspace 482.

The graphics acceleration module 446 and/or the individual graphicsprocessing engines 431-432, N can be shared by all or a subset of theprocesses in the system. Embodiments of the invention include aninfrastructure for setting up the process state and sending a WD 484 toa graphics acceleration module 446 to start a job in a virtualizedenvironment.

In one implementation, the dedicated-process programming model isimplementation-specific. In this model, a single process owns thegraphics acceleration module 446 or an individual graphics processingengine 431. Because the graphics acceleration module 446 is owned by asingle process, the hypervisor initializes the accelerator integrationcircuit 436 for the owning partition and the operating systeminitializes the accelerator integration circuit 436 for the owningprocess at the time when the graphics acceleration module 446 isassigned.

In operation, a WD fetch unit 491 in the accelerator integration slice490 fetches the next WD 484 which includes an indication of the work tobe done by one of the graphics processing engines of the graphicsacceleration module 446. Data from the WD 484 may be stored in registers445 and used by the MMU 439, interrupt management circuit 447 and/orcontext management circuit 448 as illustrated. For example, oneembodiment of the MMU 439 includes segment/page walk circuitry foraccessing segment/page tables 486 within the OS virtual address space485. The interrupt management circuit 447 may process interrupt events492 received from the graphics acceleration module 446. When performinggraphics operations, an effective address 493 generated by a graphicsprocessing engine 431-432, N is translated to a real address by the MMU439.

In one embodiment, the same set of registers 445 are duplicated for eachgraphics processing engine 431-432, N and/or graphics accelerationmodule 446 and may be initialized by the hypervisor or operating system.Each of these duplicated registers may be included in an acceleratorintegration slice 490. Exemplary registers that may be initialized bythe hypervisor are shown in Table 1.

TABLE 1 Hypervisor Initialized Registers 1 Slice Control Register 2 RealAddress (RA) Scheduled Processes Area Pointer 3 Authority Mask OverrideRegister 4 Interrupt Vector Table Entry Offset 5 Interrupt Vector TableEntry Limit 6 State Register 7 Logical Partition ID 8 Real address (RA)Hypervisor Accelerator Utilization Record Pointer 9 Storage DescriptionRegister

Exemplary registers that may be initialized by the operating system areshown in Table 2.

TABLE 2 Operating System Initialized Registers 1 Process and ThreadIdentification 2 Effective Address (EA) Context Save/Restore Pointer 3Virtual Address (VA) Accelerator Utilization Record Pointer 4 VirtualAddress (VA) Storage Segment Table Pointer 5 Authority Mask 6 Workdescriptor

In one embodiment, each WD 484 is specific to a particular graphicsacceleration module 446 and/or graphics processing engine 431-432, N. Itcontains all the information a graphics processing engine 431-432, Nrequires to do its work or it can be a pointer to a memory locationwhere the application has set up a command queue of work to becompleted.

FIG. 4E illustrates additional details for one embodiment of a sharedmodel. This embodiment includes a hypervisor real address space 498 inwhich a process element list 499 is stored. The hypervisor real addressspace 498 is accessible via a hypervisor 496 which virtualizes thegraphics acceleration module engines for the operating system 495.

The shared programming models allow for all or a subset of processesfrom all or a subset of partitions in the system to use a graphicsacceleration module 446. There are two programming models where thegraphics acceleration module 446 is shared by multiple processes andpartitions: time-sliced shared and graphics directed shared.

In this model, the system hypervisor 496 owns the graphics accelerationmodule 446 and makes its function available to all operating systems495. For a graphics acceleration module 446 to support virtualization bythe system hypervisor 496, the graphics acceleration module 446 mayadhere to the following requirements: 1) An application's job requestmust be autonomous (that is, the state does not need to be maintainedbetween jobs), or the graphics acceleration module 446 must provide acontext save and restore mechanism. 2) An application's job request isguaranteed by the graphics acceleration module 446 to complete in aspecified amount of time, including any translation faults, or thegraphics acceleration module 446 provides the ability to preempt theprocessing of the job. 3) The graphics acceleration module 446 must beguaranteed fairness between processes when operating in the directedshared programming model.

In one embodiment, for the shared model, the application 480 is requiredto make an operating system 495 system call with a graphics accelerationmodule 446 type, a work descriptor (WD), an authority mask register(AMR) value, and a context save/restore area pointer (CSRP). Thegraphics acceleration module 446 type describes the targetedacceleration function for the system call. The graphics accelerationmodule 446 type may be a system-specific value. The WD is formattedspecifically for the graphics acceleration module 446 and can be in theform of a graphics acceleration module 446 command, an effective addresspointer to a user-defined structure, an effective address pointer to aqueue of commands, or any other data structure to describe the work tobe done by the graphics acceleration module 446. In one embodiment, theAMR value is the AMR state to use for the current process. The valuepassed to the operating system is similar to an application setting theAMR. If the accelerator integration circuit 436 and graphicsacceleration module 446 implementations do not support a User AuthorityMask Override Register (UAMOR), the operating system may apply thecurrent UAMOR value to the AMR value before passing the AMR in thehypervisor call. The hypervisor 496 may optionally apply the currentAuthority Mask Override Register (AMOR) value before placing the AMRinto the process element 483. In one embodiment, the CSRP is one of theregisters 445 containing the effective address of an area in theapplication's address space 482 for the graphics acceleration module 446to save and restore the context state. This pointer is optional if nostate is required to be saved between jobs or when a job is preempted.The context save/restore area may be pinned system memory.

Upon receiving the system call, the operating system 495 may verify thatthe application 480 has registered and been given the authority to usethe graphics acceleration module 446. The operating system 495 thencalls the hypervisor 496 with the information shown in Table 3.

TABLE 3 OS to Hypervisor Call Parameters 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 The virtual address of the storagesegment table pointer (SSTP) 7 A logical interrupt service number (LISN)

Upon receiving the hypervisor call, the hypervisor 496 verifies that theoperating system 495 has registered and been given the authority to usethe graphics acceleration module 446. The hypervisor 496 then puts theprocess element 483 into the process element linked list for thecorresponding graphics acceleration module 446 type. The process elementmay include the information shown in Table 4.

TABLE 4 Process Element Information 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 The virtual address of the storagesegment table pointer (SSTP) 7 A logical interrupt service number (LISN)8 Interrupt vector table, derived from the hypervisor call parameters. 9A state register (SR) value 10 A logical partition ID (LPID) 11 A realaddress (RA) hypervisor accelerator utilization record pointer 12 TheStorage Descriptor Register (SDR)

In one embodiment, the hypervisor initializes a plurality of acceleratorintegration slice 490 registers 445.

As illustrated in FIG. 4F, one embodiment of the invention employs aunified memory addressable via a common virtual memory address spaceused to access the physical processor memories 401-402 and GPU memories420-423. In this implementation, operations executed on the GPUs 410-413utilize the same virtual/effective memory address space to access theprocessors memories 401-402 and vice versa, thereby simplifyingprogrammability. In one embodiment, a first portion of thevirtual/effective address space is allocated to the processor memory401, a second portion to the second processor memory 402, a thirdportion to the GPU memory 420, and so on. The entire virtual/effectivememory space (sometimes referred to as the effective address space) isthereby distributed across each of the processor memories 401-402 andGPU memories 420-423, allowing any processor or GPU to access anyphysical memory with a virtual address mapped to that memory.

In one embodiment, bias/coherence management circuitry 494A-494E withinone or more of the MMUs 439A-439E ensures cache coherence between thecaches of the host processors (e.g., 405) and the GPUs 410-413 andimplements biasing techniques indicating the physical memories in whichcertain types of data should be stored. While multiple instances ofbias/coherence management circuitry 494A-494E are illustrated in FIG.4F, the bias/coherence circuitry may be implemented within the MMU ofone or more host processors 405 and/or within the acceleratorintegration circuit 436.

One embodiment allows GPU-attached memory 420-423 to be mapped as partof system memory, and accessed using shared virtual memory (SVM)technology, but without suffering the typical performance drawbacksassociated with full system cache coherence. The ability to GPU-attachedmemory 420-423 to be accessed as system memory without onerous cachecoherence overhead provides a beneficial operating environment for GPUoffload. This arrangement allows the host processor 405 software tosetup operands and access computation results, without the overhead oftradition I/O DMA data copies. Such traditional copies involve drivercalls, interrupts and memory mapped I/O (MMIO) accesses that are allinefficient relative to simple memory accesses. At the same time, theability to access GPU attached memory 420-423 without cache coherenceoverheads can be critical to the execution time of an offloadedcomputation. In cases with substantial streaming write memory traffic,for example, cache coherence overhead can significantly reduce theeffective write bandwidth seen by a GPU 410-413. The efficiency ofoperand setup, the efficiency of results access, and the efficiency ofGPU computation all play a role in determining the effectiveness of GPUoffload.

In one implementation, the selection of between GPU bias and hostprocessor bias is driven by a bias tracker data structure. A bias tablemay be used, for example, which may be a page-granular structure (i.e.,controlled at the granularity of a memory page) that includes 1 or 2bits per GPU-attached memory page. The bias table may be implemented ina stolen memory range of one or more GPU-attached memories 420-423, withor without a bias cache in the GPU 410-413 (e.g., to cachefrequently/recently used entries of the bias table). Alternatively, theentire bias table may be maintained within the GPU.

In one implementation, the bias table entry associated with each accessto the GPU-attached memory 420-423 is accessed prior the actual accessto the GPU memory, causing the following operations. First, localrequests from the GPU 410-413 that find their page in GPU bias areforwarded directly to a corresponding GPU memory 420-423. Local requestsfrom the GPU that find their page in host bias are forwarded to theprocessor 405 (e.g., over a high-speed link as discussed above). In oneembodiment, requests from the processor 405 that find the requested pagein host processor bias complete the request like a normal memory read.Alternatively, requests directed to a GPU-biased page may be forwardedto the GPU 410-413. The GPU may then transition the page to a hostprocessor bias if it is not currently using the page.

The bias state of a page can be changed either by a software-basedmechanism, a hardware-assisted software-based mechanism, or, for alimited set of cases, a purely hardware-based mechanism.

One mechanism for changing the bias state employs an API call (e.g.OpenCL), which, in turn, calls the GPU's device driver which, in turn,sends a message (or enqueues a command descriptor) to the GPU directingit to change the bias state and, for some transitions, perform a cacheflushing operation in the host. The cache flushing operation is requiredfor a transition from host processor 405 bias to GPU bias, but is notrequired for the opposite transition.

In one embodiment, cache coherency is maintained by temporarilyrendering GPU-biased pages uncacheable by the host processor 405. Toaccess these pages, the processor 405 may request access from the GPU410 which may or may not grant access right away, depending on theimplementation. Thus, to reduce communication between the processor 405and GPU 410 it is beneficial to ensure that GPU-biased pages are thosewhich are required by the GPU but not the host processor 405 and viceversa.

Graphics Processing Pipeline

FIG. 5 illustrates a graphics processing pipeline 500, according to anembodiment. In one embodiment a graphics processor can implement theillustrated graphics processing pipeline 500. The graphics processor canbe included within the parallel processing subsystems as describedherein, such as the parallel processor 200 of FIG. 2A, which, in oneembodiment, is a variant of the parallel processor(s) 112 of FIG. 1. Thevarious parallel processing systems can implement the graphicsprocessing pipeline 500 via one or more instances of the parallelprocessing unit (e.g., parallel processing unit 202 of FIG. 2A) asdescribed herein. For example, a shader unit (e.g., graphicsmultiprocessor 234 of FIG. 2C) may be configured to perform thefunctions of one or more of a vertex processing unit 504, a tessellationcontrol processing unit 508, a tessellation evaluation processing unit512, a geometry processing unit 516, and a fragment/pixel processingunit 524. The functions of data assembler 502, primitive assemblers 506,514, 518, tessellation unit 510, rasterizer 522, and raster operationsunit 526 may also be performed by other processing engines within aprocessing cluster (e.g., processing cluster 214 of FIG. 2A) and acorresponding partition unit (e.g., partition unit 220A-220N of FIG.2A). The graphics processing pipeline 500 may also be implemented usingdedicated processing units for one or more functions. In one embodiment,one or more portions of the graphics processing pipeline 500 can beperformed by parallel processing logic within a general purposeprocessor (e.g., CPU). In one embodiment, one or more portions of thegraphics processing pipeline 500 can access on-chip memory (e.g.,parallel processor memory 222 as in FIG. 2A) via a memory interface 528,which may be an instance of the memory interface 218 of FIG. 2A.

In one embodiment the data assembler 502 is a processing unit thatcollects vertex data for surfaces and primitives. The data assembler 502then outputs the vertex data, including the vertex attributes, to thevertex processing unit 504. The vertex processing unit 504 is aprogrammable execution unit that executes vertex shader programs,lighting and transforming vertex data as specified by the vertex shaderprograms. The vertex processing unit 504 reads data that is stored incache, local or system memory for use in processing the vertex data andmay be programmed to transform the vertex data from an object-basedcoordinate representation to a world space coordinate space or anormalized device coordinate space.

A first instance of a primitive assembler 506 receives vertex attributesfrom the vertex processing unit 504. The primitive assembler 506readings stored vertex attributes as needed and constructs graphicsprimitives for processing by tessellation control processing unit 508.The graphics primitives include triangles, line segments, points,patches, and so forth, as supported by various graphics processingapplication programming interfaces (APIs).

The tessellation control processing unit 508 treats the input verticesas control points for a geometric patch. The control points aretransformed from an input representation from the patch (e.g., thepatch's bases) to a representation that is suitable for use in surfaceevaluation by the tessellation evaluation processing unit 512. Thetessellation control processing unit 508 can also compute tessellationfactors for edges of geometric patches. A tessellation factor applies toa single edge and quantifies a view-dependent level of detail associatedwith the edge. A tessellation unit 510 is configured to receive thetessellation factors for edges of a patch and to tessellate the patchinto multiple geometric primitives such as line, triangle, orquadrilateral primitives, which are transmitted to a tessellationevaluation processing unit 512. The tessellation evaluation processingunit 512 operates on parameterized coordinates of the subdivided patchto generate a surface representation and vertex attributes for eachvertex associated with the geometric primitives.

A second instance of a primitive assembler 514 receives vertexattributes from the tessellation evaluation processing unit 512, readingstored vertex attributes as needed, and constructs graphics primitivesfor processing by the geometry processing unit 516. The geometryprocessing unit 516 is a programmable execution unit that executesgeometry shader programs to transform graphics primitives received fromprimitive assembler 514 as specified by the geometry shader programs. Inone embodiment the geometry processing unit 516 is programmed tosubdivide the graphics primitives into one or more new graphicsprimitives and calculate parameters used to rasterize the new graphicsprimitives.

In some embodiments the geometry processing unit 516 can add or deleteelements in the geometry stream. The geometry processing unit 516outputs the parameters and vertices specifying new graphics primitivesto primitive assembler 518. The primitive assembler 518 receives theparameters and vertices from the geometry processing unit 516 andconstructs graphics primitives for processing by a viewport scale, cull,and clip unit 520. The geometry processing unit 516 reads data that isstored in parallel processor memory or system memory for use inprocessing the geometry data. The viewport scale, cull, and clip unit520 performs clipping, culling, and viewport scaling and outputsprocessed graphics primitives to a rasterizer 522.

The rasterizer 522 can perform depth culling and other depth-basedoptimizations. The rasterizer 522 also performs scan conversion on thenew graphics primitives to generate fragments and output those fragmentsand associated coverage data to the fragment/pixel processing unit 524.The fragment/pixel processing unit 524 is a programmable execution unitthat is configured to execute fragment shader programs or pixel shaderprograms. The fragment/pixel processing unit 524 transforming fragmentsor pixels received from rasterizer 522, as specified by the fragment orpixel shader programs. For example, the fragment/pixel processing unit524 may be programmed to perform operations included but not limited totexture mapping, shading, blending, texture correction and perspectivecorrection to produce shaded fragments or pixels that are output to araster operations unit 526. The fragment/pixel processing unit 524 canread data that is stored in either the parallel processor memory or thesystem memory for use when processing the fragment data. Fragment orpixel shader programs may be configured to shade at sample, pixel, tile,or other granularities depending on the sampling rate configured for theprocessing units.

The raster operations unit 526 is a processing unit that performs rasteroperations including, but not limited to stencil, z test, blending, andthe like, and outputs pixel data as processed graphics data to be storedin graphics memory (e.g., parallel processor memory 222 as in FIG. 2A,and/or system memory 104 as in FIG. 1), to be displayed on the one ormore display device(s) 110 or for further processing by one of the oneor more processor(s) 102 or parallel processor(s) 112. In someembodiments the raster operations unit 526 is configured to compress zor color data that is written to memory and decompress z or color datathat is read from memory.

Coarse Grain Coherency

Embodiments described herein can drastically reduce the system snoopbandwidth required for hardware coherent GPGPU/CPU memory accesses, thusimproving performance and reducing power for heterogeneous processingapplications. In one embodiment memory coherency is supported at largegranularity in the range of 1 kilobytes to 4 kilobytes. Large graincoherency as described herein differs from the cache-line coherency(e.g., 64-bytes) used in existing implementations. In one embodiment acombination of snoop-based and directory-based coherency is used toenable hardware based CPU and GPGPU cache coherence. The GPGPU and theCPU can each implement an “ownership table” to track ownership foraccess permission to a given memory region. An agent (CPU or GPU)obtains ownership of a region by sending a region snoop to the systemthat defines a region in memory for which the agent is requestingownership. Once the ownership of the region is obtained by the agent andlogged in an ownership table associated with the agent, subsequentaccesses to the memory region can be performed without any snooprequests.

FIG. 6 illustrates a heterogeneous processing system 600 havingheterogeneous hardware coherency, according to an embodiment. In oneembodiment the heterogeneous processing system 600 includes multipleprocessors (CPU 602A-602D) and a GPGPU 620. Each CPU 602A-602D and theGPGPU 620 have access to shared virtual memory (SVM). In one embodimentany CPU 602A-602D can allocate a surface in system memory 640. Thesurface can then be made accessible to the GPGPU 620 by mapping theaddress of the surface into the virtual memory space of the GPGPU 620.In one embodiment the CPUs 602A-602D and the GPGPU 620 can access thesame physical memory via different virtual memory addresses. In oneembodiment the CPUs 602A-602D and the GPGPU 620 have a unified virtualmemory space in which at least a portion of the memory 640 can beaccessed by a CPU 602A-602D and the GPGPU 620 using the same virtualaddress. With unified virtual memory, a process on a CPU 602A-602D canpass a pointer to a surface to the GPGPU 620

Each CPU 602A-602D can include a level 1 cache (L1 cache 604), a level 2(L2 cache 606). The L1 cache 604 can include separate portions forinstructions and data. In one embodiment each CPU includes multiplecores. In such embodiment, each core has a separate instance of the L1cache 604, while the L2 cache 606 is shared between the cores. A lastlevel cache (LLC 610) is shared between all CPUs 602A-602D. The multipleCPUs 602A-602D can share data between themselves and with the GPGPU 620via the LLC 610.

The GPGPU 620 includes an L1 cache 621, L2 cache 622, and a level 3cache (L3 cache 622). The L1 cache 621 can include separate portions forinstructions and data, or can be a unified cache. The L2 cache 622 caninclude multiple caches, including a render cache or a depth cache. Inone embodiment the GPGPU 620 also includes local memory 630. The localmemory 630 can be on-board DRAM memory, for example, where the GPGPU 620is an add-in card or a multi-chip module. In one embodiment the localmemory 630 is SRAM memory that is on the die of the GPGPU 620. The localmemory 630 can also be an embedded DRAM memory on package with the GPGPU620.

Each cache memory in the CPUs 602A-602D and the GPGPU 620 is managed ona cache line basis. The number of cache lines within a cache isdependent upon the size of the cache and the size of each cache line.The size of the cache lines can vary across embodiments and the variouscaches may not use the same size cache lines. In one embodiment, eachcache line of the CPUs 602A-602D is 64 bytes, although not allembodiments are limited to 64-byte cache lines. In such embodiment theGPGPU 620 also includes 64-byte cache lines.

Enabling hardware managed cache coherency between the CPUs 602A-602D andthe GPGPU 620 would significantly simplify programming heterogeneousworkloads on a heterogeneous processing systems. However, the snoopoverhead introduced by per-cache line coherency can introducesignificant overhead into the system. Embodiments described hereinenable hardware coherency between the CPUs 602A-602D and the GPGPU 620via coarse grained coherency, in which memory coherency is supported atthe kilobyte granularity. Coarse grained coherency between the GPGPU 620and the multiple CPUs 602A-603D can be enabled via the use of a supercache line (e.g., superline). A superline is a coarse grain coherencyunit that includes multiple cache lines. The size of the superline canvary across embodiments. In one embodiment the superline is a 1 kilobyteregion. In one embodiment the superline is a 4 kilobyte region. In oneembodiment the size of the superline is configurable to N multiples ofthe size of a cache line. For example, with a cache line size of 64bytes, a 1 kilobyte superline can be configured by setting N=16.

Coherency at the superline level can be enabled via multiple datastructures and dedicated cache memories. In one embodiment coherency isenabled via the use of a superline ownership table (SLOT 632), asuperline directory table (SDT 642), and a superline directory tablecache (SDTC 612). The SLOT 632 can be stored in the local memory 630 ofthe GPGPU 620. The SDT 642 can be stored in system memory 640 that isaccessible by the CPUs 602A-602D and the GPGPU 620. The SDTC 612 is acache memory that can store superline directory table entries. In oneembodiment the SDTC 612 is an on-die cache memory of the CPUs 602A-602D.In one embodiment, the SDTC 612 can also be accessed by the GPGPU 620and may be shared between the CPUs 602A-602D and the GPGPU 620.

In one embodiment the SLOT 632 is used to indicate superline ownershipfor the GPGPU 620. For cache activity within a superline owned by theGPU, no coherency flow is required and snoop data is not required to bebroadcast by the GPGPU 620. In one embodiment the SLOT 632 can track ifany cache lines within a superline are cached within the L3 cache 624.While cache coherency can be tracked at the superline level, the L3cache 624 can continue to operate at cache line granularity. In oneembodiment, one or more internal caches within the CPUs 602A-602D andthe GPGPU 620 are not cache coherent. For example, global heterogeneouscoarse grained system coherence may be limited to data stored within theL3 cache 623 of the GPGPU 620 and the last level cache 610 sharedbetween the CPUs 602A-602D and the GPGPU 620. In such embodiment, aregion of the memory 640 can be declared shared virtual memory that willbe globally coherent and data within the shared virtual memory may becached only in cache memories that participate in the globalheterogeneous coarse grain coherency system, bypassing lower level cachememories in the GPGPU 620 and/or the CPUs 602A-602D.

The SDT 642 is a table that indicates superline ownership of superlinesstored within DRAM. In one embodiment the SDT 642 is stored in “stolen”memory that is accessible by the GPGPU 620 and the CPUs 602A-602D but isnot accessible to the operating system executing on the CPU. In suchembodiment the SDT 642 is limited to listing ownership of superlinesoutside of stolen memory. The size of the SDT 642 can vary based on thesize of a superline and the size of memory 640. For example and in oneembodiment, with a 2 kilobyte superline a SDT 642 of approximately 2megabytes can cover 8 gigabytes of memory. Entries within the SDT 642can be cached within the SDTC 612. The SDTC 612 can function in asimilar manner as a TLB that is shared between the GPGPU 620 and theCPUs 602A-602D, with entries for the SDT 642 cached in the SDTC 612 in asimilar manner as page table entries from a page table are stored in aTLB.

For an agent (one of the CPUs 602A-602D or the GPGPU 620) on theheterogeneous processing system 600 to take ownership of a superline,the agent can send a region snoop to the system. Once the ownership ofthe region (e.g., one or more superlines) is obtained and logged in therespective ownership tables (e.g., SDT 642, SLOT 632), subsequentaccesses to the memory region can be done without requiring ortriggering snoop requests. In one embodiment the region snoop can beperformed via a request for ownership operation. The request forownership operation can be used when an agent (e.g., the GPGPU 620) willattempt to perform cache line writes to a superline for which the agentdoes not have current ownership.

FIG. 7 illustrates logical structures used for coarse grain coherency,according to an embodiment. In embodiments described herein, coarsegrain coherency can be enabled in a heterogeneous processing system(e.g., heterogeneous processing system 600 of FIG. 6) at the granularityof a superline 710. The superline is a region of memory that is multiplecache lines in size (e.g., cache line A-N). An agent (CPU, GPU, etc.)within the system can send a region snoop for an address range. Based onthe size of the address range and the degree of associativity of thecache memories within the system, N cache lines in various processorcaches may be mapped to the address range specified by the superline. Inone embodiment, all cache memories having cache lines within the superline will map those cache lines to a single coherency state.

Superlines owned by a GPGPU can be tracked via entries in a superlineownership table (e.g., SLOT 632 of FIG. 6). An exemplary SLOT entry 720includes a superline tag (SL tag 722), a set of coherency protocolstatus bits (e.g., MESI 724), a set of per cache line valid bits 726,and least recently used (LRU) status bits 728. The SL tag 722 is used toidentify the superline 710 for which the SLOT entry 720 is associated.The cache coherency protocol status bits track states associated withthe cache coherency protocol in use by the coarse grain coherencysystem. MESI 724 (modified, exclusive, shared, invalid) is used by theillustrated embodiment, however, other embodiments can use othercoherency protocols, with the coherency behavior of the embodimentadapted accordingly. For example, a modified, exclusive, shared,invalid, forwarding (MESIF) protocol can be used to enablecache-to-cache forwarding of shared cache lines. The per cache linevalid bits 726 indicate a valid or invalid status for each cache linewithin the superline 710. The LRU field 728 bits keep track of a leastrecently used status for the SLOT entry 720 for any caches that storethe entry. For example and in one embodiment, the SLOT entry 720 for asuperline can be cached within a coherency cache within the GPGPU.

An SDT entry 730 can be stored in a superline directory table in memory(e.g., SDT 642 of FIG. 6). In one embodiment the SDT entry 730 is athree bit entry that is stored for every superline in memory. For eachsuperline, a shared/exclusive bit (S/E 732) is used to indicate if thesuperline is in a shared state or in an exclusive state. A CPU bit 734and a GPU bit 736 indicate whether ownership resides within a CPU or aGPU.

An SDTC entry 740 can be stored in the superline directory table cache(SDTC 612), which is a cache memory stored within the processor die of aCPU. The SDTC entry 740 includes a superline tag (SL tag 742) toidentify a superline, cached data from an SDT entry 730 (e.g., S/E 732,CPU bit 734, GPU bit 736), and an LRU field 748. In one embodiment theSL tag 742 is generated based on the address of the superline. In oneembodiment the SDT entry 730 is indexed based on a superline tag and theSL tag 742 stored in the SDTC entry 740 is the superline tag thatindexes the SDT entry 730 for which data is cached. The LRU field 748for the SDTC entry 740 is used to determine which entry to evict fromthe SDTC when replacing cache entries, where the least recently usedentry is evicted from the SDTC.

In embodiments in which a MESI or MESI compatible coherency protocol isenabled for global heterogeneous coherency, a superline may only bewritten if the superline is in the modified or exclusive state. If thesuperline is in the shared state, all cache lines associated with thesuperline are invalidated before an agent performs a write with globalcoherence. A cache that holds a cache line from a superline that is inthe modified state snoops attempted reads from other globally coherentcaches in the system for the corresponding main memory location andinsert the data that it held in the cache. In one embodiment thisoperation can be performed by causing the snooped read to back off,updating the data stored in memory, then changing the superline statusto a shared state. In one embodiments, some agents are not allowed tomaintain a superline in the modified state. For example and in oneembodiment the GPGPU 620 can maintain a superline in the modified state,while a CPU 602A-602D is limited to a shared or exclusive state. A cachethat holds a cache line associated with a superline in the shared statecan listen for invalidate or request-for-ownership broadcasts from otherglobally coherent caches, and discard the cache line if the associatedsuperline is invalidated. In one embodiment, when ownership of asuperline is taken by an agent, all cache lines associated with thesuperline are transitioned to an exclusive state.

FIG. 8 is a flow diagram of logic 800 to enable coarse grain coherencyin a heterogeneous processing system. The logic 800 can be distributedacross cache management systems of a heterogeneous processing system(e.g., heterogeneous processing system 600 of FIG. 6), including an L3cache within a GPGPU, a last level cache shared between CPUs and GPGPU,and one or more memory controllers within the system.

In one embodiment the logic 800 can request to access a virtual memoryaddress from a process executing on an agent of the heterogeneousprocessing system, as shown at block 802. In response, the logic 800 candetermine if the agent has ownership of a superline associated with thevirtual address, as shown at block 804. At least one of the multiplecache lines in the superline is associated with the requested virtualmemory address. The logic 800 can then determine if the agent owns thesuperline at block 805. The logic 800 can determine ownership for thesuperline by checking an ownership directory associated with the agent.With reference to FIG. 6, where the agent is a GPGPU, the agent cancheck a locally stored superline ownership table (e.g., SLOT 632). Wherethe agent is a CPU, the agent can check a superline directory tablecache (e.g., SDTC 612) or a superline directory table (e.g., SDT 642) ifthe access to the superline directory table cache misses.

Returning to FIG. 8, if the agent owns the superline, the logic 800 canallow the agent can access the virtual memory address as shown at block810. Access to the virtual memory address when the address is within therange of an owned superline can be performed without causing orrequiring global snoop requests. In this scenario, any other agentshaving cache lines associated with an address within the superline rangewill have invalidated those cache lines upon ownership transfer to theagent. If the agent does not own the cache line, as determined at block805, the logic 800 can send a region snoop to acquire ownership of thememory region including the virtual memory address, as shown at block806. The region snoop can indicate that the agent intends to takeownership of any cache lines associated with the superlines within thesnooped region. In one embodiment the region snoop can cause multiplesuperlines to change ownership.

Changing superline ownership can cause any cache lines held by otheragents within the global heterogeneous coarse grained coherency systemto be invalidated. Any dirty data within those caches may be evicted andwritten back to memory and, in some embodiments, forwarded to othercache memories. Once ownership is acquired, the logic 800 can then setthe superline to an owned state within the directory or ownership tablewithin the agent, as shown at block 808. The specific owned state canvary across embodiments. In one embodiment the owned state is anexclusive state that can be set within a SLOT, SDT, or SDTC entry, asshown in FIG. 7. Setting the superline to the owned state can allow theagent to access the virtual memory address at block 810.

FIG. 9 is a block diagram of a data processing system 900, according toan embodiment. The data processing system 900 is a heterogeneousprocessing system having a processor 902, unified memory 910, and aGPGPU 920. The processor 902 can be any of the general-purposeprocessors or central processing units (CPUs) described herein. TheGPGPU 920 can be any of the GPGPUs or parallel processors describedherein. In one embodiment the processor 902 and GPGPU 920 are integratedwithin a system on a chip integrated circuit. In one embodiment theprocessor 902 and the GPGPU 920 are attached to separate system boards.The processor 902 may be connected or soldered to a mainboard socket.The GPGPU 920 can reside on a board of an add-in graphics card deviceand connected to the system via a system bus, such as a PCIe bus.

In one embodiment the processor 902 includes a cache memory 904 and acoherency module 905. The cache memory 904 can coherently cache datastored in coherent shared virtual memory. Coherency operations for theprocessor 902 can be performed by a coherency module 905. The coherencymodule 905 can perform any of the CPU-side coherency operationsdescribed herein.

The processor 902 can execute instructions for a compiler 915 stored insystem memory 912. The compiler 915 executes on the processor 902 tocompile source code 914A into compiled code 914B. The compiled code 914Bcan include code that may be executed by the processor 902 and/or codethat may be executed by the GPGPU 920. During compilation, the compiler915 can perform operations to insert metadata regarding variouscharacteristics and metrics related to the source code 914A and compiledsource code 914B. The compiler 915 can include the information necessaryto perform such operations or the operations can be performed with theassistance of a runtime library 916. The runtime library 916 can alsofacilitate the compiler 915 in the compilation of the source code 914Aand can also include instructions that are linked at runtime with thecompiled code 914B to facilitate execution on the GPGPU 920.

The unified memory 910 represents a unified address space that may beaccessed by the processor 902 and the GPGPU 920. The unified memoryincludes system memory 912 as well as GPGPU memory 918. The GPGPU memory918 includes GPGPU local memory 928 within the GPGPU 920 and can alsoinclude some or all of system memory 912. For example, compiled code914B stored in system memory 912 can also be mapped into GPGPU memory918 for access by the GPGPU 920.

The GPGPU 920 includes multiple compute blocks 921A-921N, which caninclude multiple compute clusters, such as the processing cluster 214 ofFIG. 2C. The GPGPU 920 also includes a set of registers 924, cachememory 926, and a coherency module 925 that can be shared among thecompute blocks 921A-921N. The coherency module 925 can be configured tomanage coherency operations on the GPGPU 920 to enable globalheterogeneous coarse grain coherency for shared virtual memory. TheGPGPU 920 can additionally include GPGPU local memory 928, which isphysical memory that shares a graphics card or multi-chip module withthe GPGPU 920.

In one embodiment the compute blocks 921A-921N each include a TLB922A-922N and at least one cache 923A-923N that is shared among thecompute clusters within the compute blocks 921A-921N. The commonresources that are shared among compute elements of the compute blockscan be leveraged efficiently by attempting to schedule threads that willaccess common data to the same compute block. In one embodiment a globalheterogeneous coarse grained coherency system can be enabled at theGPGPU 920 via the coherency module 925, which may perform any of the GPUside coarse grained coherency operations described herein. Global cachecoherency can be maintained between one or more cache memories withinthe GPGPU 920 (e.g., cache 923A-923N and/or shared cache 926) and thecache memory 904 within the processor 902.

Additional Exemplary Graphics Processing System

Details of the embodiments described above can be incorporated withingraphics processing systems and devices described below. The graphicsprocessing system and devices of FIG. 10 through FIG. 23 illustratealternative systems and graphics processing hardware that can implementany and all of the techniques described above.

Additional Exemplary Graphics Processing System Overview

FIG. 10 is a block diagram of a processing system 1000, according to anembodiment. In various embodiments the system 1000 includes one or moreprocessors 1002 and one or more graphics processors 1008, and may be asingle processor desktop system, a multiprocessor workstation system, ora server system having a large number of processors 1002 or processorcores 1007. In one embodiment, the system 1000 is a processing platformincorporated within a system-on-a-chip (SoC) integrated circuit for usein mobile, handheld, or embedded devices.

An embodiment of system 1000 can include, or be incorporated within aserver-based gaming platform, a game console, including a game and mediaconsole, a mobile gaming console, a handheld game console, or an onlinegame console. In some embodiments system 1000 is a mobile phone, smartphone, tablet computing device or mobile Internet device. Dataprocessing system 1000 can also include, couple with, or be integratedwithin a wearable device, such as a smart watch wearable device, smarteyewear device, augmented reality device, or virtual reality device. Insome embodiments, data processing system 1000 is a television or set topbox device having one or more processors 1002 and a graphical interfacegenerated by one or more graphics processors 1008.

In some embodiments, the one or more processors 1002 each include one ormore processor cores 1007 to process instructions which, when executed,perform operations for system and user software. In some embodiments,each of the one or more processor cores 1007 is configured to process aspecific instruction set 1009. In some embodiments, instruction set 1009may facilitate Complex Instruction Set Computing (CISC), ReducedInstruction Set Computing (RISC), or computing via a Very LongInstruction Word (VLIW). Multiple processor cores 1007 may each processa different instruction set 1009, which may include instructions tofacilitate the emulation of other instruction sets. Processor core 1007may also include other processing devices, such a Digital SignalProcessor (DSP).

In some embodiments, the processor 1002 includes cache memory 1004.Depending on the architecture, the processor 1002 can have a singleinternal cache or multiple levels of internal cache. In someembodiments, the cache memory is shared among various components of theprocessor 1002. In some embodiments, the processor 1002 also uses anexternal cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC))(not shown), which may be shared among processor cores 1007 using knowncache coherency techniques. A register file 1006 is additionallyincluded in processor 1002 which may include different types ofregisters for storing different types of data (e.g., integer registers,floating point registers, status registers, and an instruction pointerregister). Some registers may be general-purpose registers, while otherregisters may be specific to the design of the processor 1002.

In some embodiments, processor 1002 is coupled with a processor bus 1010to transmit communication signals such as address, data, or controlsignals between processor 1002 and other components in system 1000. Inone embodiment the system 1000 uses an exemplary ‘hub’ systemarchitecture, including a memory controller hub 1016 and an Input Output(I/O) controller hub 1030. A memory controller hub 1016 facilitatescommunication between a memory device and other components of system1000, while an I/O Controller Hub (ICH) 1030 provides connections to I/Odevices via a local I/O bus. In one embodiment, the logic of the memorycontroller hub 1016 is integrated within the processor.

Memory device 1020 can be a dynamic random access memory (DRAM) device,a static random access memory (SRAM) device, flash memory device,phase-change memory device, or some other memory device having suitableperformance to serve as process memory. In one embodiment the memorydevice 1020 can operate as system memory for the system 1000, to storedata 1022 and instructions 1021 for use when the one or more processors1002 executes an application or process. Memory controller hub 1016 alsocouples with an optional external graphics processor 1012, which maycommunicate with the one or more graphics processors 1008 in processors1002 to perform graphics and media operations.

In some embodiments, ICH 1030 enables peripherals to connect to memorydevice 1020 and processor 1002 via a high-speed I/O bus. The I/Operipherals include, but are not limited to, an audio controller 1046, afirmware interface 1028, a wireless transceiver 1026 (e.g., Wi-Fi,Bluetooth), a data storage device 1024 (e.g., hard disk drive, flashmemory, etc.), and a legacy I/O controller 1040 for coupling legacy(e.g., Personal System 2 (PS/2)) devices to the system. One or moreUniversal Serial Bus (USB) controllers 1042 connect input devices, suchas keyboard and mouse 1044 combinations. A network controller 1034 mayalso couple with ICH 1030. In some embodiments, a high-performancenetwork controller (not shown) couples with processor bus 1010. It willbe appreciated that the system 1000 shown is exemplary and not limiting,as other types of data processing systems that are differentlyconfigured may also be used. For example, the I/O controller hub 1030may be integrated within the one or more processor 1002, or the memorycontroller hub 1016 and I/O controller hub 1030 may be integrated into adiscreet external graphics processor, such as the external graphicsprocessor 1012.

FIG. 11 is a block diagram of an embodiment of a processor 1100 havingone or more processor cores 1102A-1102N, an integrated memory controller1114, and an integrated graphics processor 1108. Those elements of FIG.11 having the same reference numbers (or names) as the elements of anyother figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such. Processor1100 can include additional cores up to and including additional core1102N represented by the dashed lined boxes. Each of processor cores1102A-1102N includes one or more internal cache units 1104A-1104N. Insome embodiments each processor core also has access to one or moreshared cached units 1106.

The internal cache units 1104A-1104N and shared cache units 1106represent a cache memory hierarchy within the processor 1100. The cachememory hierarchy may include at least one level of instruction and datacache within each processor core and one or more levels of sharedmid-level cache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), orother levels of cache, where the highest level of cache before externalmemory is classified as the LLC. In some embodiments, cache coherencylogic maintains coherency between the various cache units 1106 and1104A-1104N.

In some embodiments, processor 1100 may also include a set of one ormore bus controller units 1116 and a system agent core 1110. The one ormore bus controller units 1116 manage a set of peripheral buses, such asone or more Peripheral Component Interconnect buses (e.g., PCI, PCIExpress). System agent core 1110 provides management functionality forthe various processor components. In some embodiments, system agent core1110 includes one or more integrated memory controllers 1114 to manageaccess to various external memory devices (not shown).

In some embodiments, one or more of the processor cores 1102A-1102Ninclude support for simultaneous multi-threading. In such embodiment,the system agent core 1110 includes components for coordinating andoperating cores 1102A-1102N during multi-threaded processing. Systemagent core 1110 may additionally include a power control unit (PCU),which includes logic and components to regulate the power state ofprocessor cores 1102A-1102N and graphics processor 1108.

In some embodiments, processor 1100 additionally includes graphicsprocessor 1108 to execute graphics processing operations. In someembodiments, the graphics processor 1108 couples with the set of sharedcache units 1106, and the system agent core 1110, including the one ormore integrated memory controllers 1114. In some embodiments, a displaycontroller 1111 is coupled with the graphics processor 1108 to drivegraphics processor output to one or more coupled displays. In someembodiments, display controller 1111 may be a separate module coupledwith the graphics processor via at least one interconnect, or may beintegrated within the graphics processor 1108 or system agent core 1110.

In some embodiments, a ring based interconnect unit 1112 is used tocouple the internal components of the processor 1100. However, analternative interconnect unit may be used, such as a point-to-pointinterconnect, a switched interconnect, or other techniques, includingtechniques well known in the art. In some embodiments, graphicsprocessor 1108 couples with the ring interconnect 1112 via an I/O link1113.

The exemplary I/O link 1113 represents at least one of multiplevarieties of I/O interconnects, including an on package I/O interconnectwhich facilitates communication between various processor components anda high-performance embedded memory module 1118, such as an eDRAM module.In some embodiments, each of the processor cores 1102A-1102N andgraphics processor 1108 use embedded memory modules 1118 as a sharedLast Level Cache.

In some embodiments, processor cores 1102A-1102N are homogenous coresexecuting the same instruction set architecture. In another embodiment,processor cores 1102A-1102N are heterogeneous in terms of instructionset architecture (ISA), where one or more of processor cores 1102A-1102Nexecute a first instruction set, while at least one of the other coresexecutes a subset of the first instruction set or a differentinstruction set. In one embodiment processor cores 1102A-1102N areheterogeneous in terms of microarchitecture, where one or more coreshaving a relatively higher power consumption couple with one or morepower cores having a lower power consumption. Additionally, processor1100 can be implemented on one or more chips or as an SoC integratedcircuit having the illustrated components, in addition to othercomponents.

FIG. 12 is a block diagram of a graphics processor 1200, which may be adiscrete graphics processing unit, or may be a graphics processorintegrated with a plurality of processing cores. In some embodiments,the graphics processor communicates via a memory mapped I/O interface toregisters on the graphics processor and with commands placed into theprocessor memory. In some embodiments, graphics processor 1200 includesa memory interface 1214 to access memory. Memory interface 1214 can bean interface to local memory, one or more internal caches, one or moreshared external caches, and/or to system memory.

In some embodiments, graphics processor 1200 also includes a displaycontroller 1202 to drive display output data to a display device 1220.Display controller 1202 includes hardware for one or more overlay planesfor the display and composition of multiple layers of video or userinterface elements. In some embodiments, graphics processor 1200includes a video codec engine 1206 to encode, decode, or transcode mediato, from, or between one or more media encoding formats, including, butnot limited to Moving Picture Experts Group (MPEG) formats such asMPEG-2, Advanced Video Coding (AVC) formats such as H.264/MPEG-4 AVC, aswell as the Society of Motion Picture & Television Engineers (SMPTE)421M/VC-1, and Joint Photographic Experts Group (JPEG) formats such asJPEG, and Motion JPEG (MJPEG) formats.

In some embodiments, graphics processor 1200 includes a block imagetransfer (BLIT) engine 1204 to perform two-dimensional (2D) rasterizeroperations including, for example, bit-boundary block transfers.However, in one embodiment, 2D graphics operations are performed usingone or more components of graphics processing engine (GPE) 1210. In someembodiments, GPE 1210 is a compute engine for performing graphicsoperations, including three-dimensional (3D) graphics operations andmedia operations.

In some embodiments, GPE 310 includes a 3D pipeline 1212 for performing3D operations, such as rendering three-dimensional images and scenesusing processing functions that act upon 3D primitive shapes (e.g.,rectangle, triangle, etc.). The 3D pipeline 1212 includes programmableand fixed function elements that perform various tasks within theelement and/or spawn execution threads to a 3D/Media sub-system 1215.While 3D pipeline 1212 can be used to perform media operations, anembodiment of GPE 1210 also includes a media pipeline 1216 that isspecifically used to perform media operations, such as videopost-processing and image enhancement.

In some embodiments, media pipeline 1216 includes fixed function orprogrammable logic units to perform one or more specialized mediaoperations, such as video decode acceleration, video de-interlacing, andvideo encode acceleration in place of, or on behalf of video codecengine 1206. In some embodiments, media pipeline 1216 additionallyincludes a thread spawning unit to spawn threads for execution on3D/Media sub-system 1215. The spawned threads perform computations forthe media operations on one or more graphics execution units included in3D/Media sub-system 1215.

In some embodiments, 3D/Media subsystem 1215 includes logic forexecuting threads spawned by 3D pipeline 1212 and media pipeline 1216.In one embodiment, the pipelines send thread execution requests to3D/Media subsystem 1215, which includes thread dispatch logic forarbitrating and dispatching the various requests to available threadexecution resources. The execution resources include an array ofgraphics execution units to process the 3D and media threads. In someembodiments, 3D/Media subsystem 1215 includes one or more internalcaches for thread instructions and data. In some embodiments, thesubsystem also includes shared memory, including registers andaddressable memory, to share data between threads and to store outputdata.

Additional Exemplary Graphics Processing Engine

FIG. 13 is a block diagram of a graphics processing engine 1310 of agraphics processor in accordance with some embodiments. In oneembodiment, the graphics processing engine (GPE) 1310 is a version ofthe GPE 1210 shown in FIG. 12. Elements of FIG. 13 having the samereference numbers (or names) as the elements of any other figure hereincan operate or function in any manner similar to that describedelsewhere herein, but are not limited to such. For example, the 3Dpipeline 1212 and media pipeline 1216 of FIG. 12 are illustrated. Themedia pipeline 1216 is optional in some embodiments of the GPE 1310 andmay not be explicitly included within the GPE 1310. For example and inat least one embodiment, a separate media and/or image processor iscoupled to the GPE 1310.

In some embodiments, GPE 1310 couples with or includes a commandstreamer 1303, which provides a command stream to the 3D pipeline 1212and/or media pipelines 1216. In some embodiments, command streamer 1303is coupled with memory, which can be system memory, or one or more ofinternal cache memory and shared cache memory. In some embodiments,command streamer 1303 receives commands from the memory and sends thecommands to 3D pipeline 1212 and/or media pipeline 1216. The commandsare directives fetched from a ring buffer, which stores commands for the3D pipeline 1212 and media pipeline 1216. In one embodiment, the ringbuffer can additionally include batch command buffers storing batches ofmultiple commands. The commands for the 3D pipeline 1212 can alsoinclude references to data stored in memory, such as but not limited tovertex and geometry data for the 3D pipeline 1212 and/or image data andmemory objects for the media pipeline 1216. The 3D pipeline 1212 andmedia pipeline 1216 process the commands and data by performingoperations via logic within the respective pipelines or by dispatchingone or more execution threads to a graphics core array 1314.

In various embodiments the 3D pipeline 1212 can execute one or moreshader programs, such as vertex shaders, geometry shaders, pixelshaders, fragment shaders, compute shaders, or other shader programs, byprocessing the instructions and dispatching execution threads to thegraphics core array 1314. The graphics core array 1314 provides aunified block of execution resources. Multi-purpose execution logic(e.g., execution units) within the graphic core array 1314 includessupport for various 3D API shader languages and can execute multiplesimultaneous execution threads associated with multiple shaders.

In some embodiments the graphics core array 1314 also includes executionlogic to perform media functions, such as video and/or image processing.In one embodiment, the execution units additionally includegeneral-purpose logic that is programmable to perform parallel generalpurpose computational operations, in addition to graphics processingoperations. The general purpose logic can perform processing operationsin parallel or in conjunction with general purpose logic within theprocessor core(s) 1007 of FIG. 10 or core 1102A-1102N as in FIG. 11.

Output data generated by threads executing on the graphics core array1314 can output data to memory in a unified return buffer (URB) 1318.The URB 1318 can store data for multiple threads. In some embodimentsthe URB 1318 may be used to send data between different threadsexecuting on the graphics core array 1314. In some embodiments the URB1318 may additionally be used for synchronization between threads on thegraphics core array and fixed function logic within the shared functionlogic 1320.

In some embodiments, graphics core array 1314 is scalable, such that thearray includes a variable number of graphics cores, each having avariable number of execution units based on the target power andperformance level of GPE 1310. In one embodiment the execution resourcesare dynamically scalable, such that execution resources may be enabledor disabled as needed.

The graphics core array 1314 couples with shared function logic 1320that includes multiple resources that are shared between the graphicscores in the graphics core array. The shared functions within the sharedfunction logic 1320 are hardware logic units that provide specializedsupplemental functionality to the graphics core array 1314. In variousembodiments, shared function logic 1320 includes but is not limited tosampler 1321, math 1322, and inter-thread communication (ITC) 1323logic. Additionally, some embodiments implement one or more cache(s)1325 within the shared function logic 1320. A shared function isimplemented where the demand for a given specialized function isinsufficient for inclusion within the graphics core array 1314. Insteada single instantiation of that specialized function is implemented as astand-alone entity in the shared function logic 1320 and shared amongthe execution resources within the graphics core array 1314. The preciseset of functions that are shared between the graphics core array 1314and included within the graphics core array 1314 varies betweenembodiments.

FIG. 14 is a block diagram of another embodiment of a graphics processor1400. Elements of FIG. 14 having the same reference numbers (or names)as the elements of any other figure herein can operate or function inany manner similar to that described elsewhere herein, but are notlimited to such.

In some embodiments, graphics processor 1400 includes a ringinterconnect 1402, a pipeline front-end 1404, a media engine 1437, andgraphics cores 1480A-1480N. In some embodiments, ring interconnect 1402couples the graphics processor to other processing units, includingother graphics processors or one or more general-purpose processorcores. In some embodiments, the graphics processor is one of manyprocessors integrated within a multi-core processing system.

In some embodiments, graphics processor 1400 receives batches ofcommands via ring interconnect 1402. The incoming commands areinterpreted by a command streamer 1403 in the pipeline front-end 1404.In some embodiments, graphics processor 1400 includes scalable executionlogic to perform 3D geometry processing and media processing via thegraphics core(s) 1480A-1480N. For 3D geometry processing commands,command streamer 1403 supplies commands to geometry pipeline 1436. Forat least some media processing commands, command streamer 1403 suppliesthe commands to a video front-end 1434, which couples with a mediaengine 1437. In some embodiments, media engine 1437 includes a VideoQuality Engine (VQE) 1430 for video and image post-processing and amulti-format encode/decode (MFX) 1433 engine to providehardware-accelerated media data encode and decode. In some embodiments,geometry pipeline 1436 and media engine 1437 each generate executionthreads for the thread execution resources provided by at least onegraphics core 1480A.

In some embodiments, graphics processor 1400 includes scalable threadexecution resources featuring modular cores 1480A-1480N (sometimesreferred to as core slices), each having multiple sub-cores 1450A-550N,1460A-1460N (sometimes referred to as core sub-slices). In someembodiments, graphics processor 1400 can have any number of graphicscores 1480A through 1480N. In some embodiments, graphics processor 1400includes a graphics core 1480A having at least a first sub-core 1450Aand a second sub-core 1460A. In other embodiments, the graphicsprocessor is a low power processor with a single sub-core (e.g., 1450A).In some embodiments, graphics processor 1400 includes multiple graphicscores 1480A-1480N, each including a set of first sub-cores 1450A-1450Nand a set of second sub-cores 1460A-1460N. Each sub-core in the set offirst sub-cores 1450A-1450N includes at least a first set of executionunits 1452A-1452N and media/texture samplers 1454A-1454N. Each sub-corein the set of second sub-cores 1460A-1460N includes at least a secondset of execution units 1462A-1462N and samplers 1464A-1464N. In someembodiments, each sub-core 1450A-1450N, 1460A-1460N shares a set ofshared resources 1470A-1470N. In some embodiments, the shared resourcesinclude shared cache memory and pixel operation logic. Other sharedresources may also be included in the various embodiments of thegraphics processor.

Additional Exemplary Execution Units

FIG. 15 illustrates thread execution logic 1500 including an array ofprocessing elements employed in some embodiments of a GPE. Elements ofFIG. 15 having the same reference numbers (or names) as the elements ofany other figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such.

In some embodiments, thread execution logic 1500 includes a shaderprocessor 1502, a thread dispatcher 1504, instruction cache 1506, ascalable execution unit array including a plurality of execution units1508A-1508N, a sampler 1510, a data cache 1512, and a data port 1514. Inone embodiment the scalable execution unit array can dynamically scaleby enabling or disabling one or more execution units (e.g., any ofexecution unit 1508A, 1508B, 1508C, 1508D, through 1508N-1 and 1508N)based on the computational requirements of a workload. In one embodimentthe included components are interconnected via an interconnect fabricthat links to each of the components. In some embodiments, threadexecution logic 1500 includes one or more connections to memory, such assystem memory or cache memory, through one or more of instruction cache1506, data port 1514, sampler 1510, and execution units 1508A-1508N. Insome embodiments, each execution unit (e.g. 1508A) is a stand-aloneprogrammable general purpose computational unit that is capable ofexecuting multiple simultaneous hardware threads while processingmultiple data elements in parallel for each thread. In variousembodiments, the array of execution units 1508A-1508N is scalable toinclude any number individual execution units.

In some embodiments, the execution units 1508A-1508N are primarily usedto execute shader programs. A shader processor 1502 can process thevarious shader programs and dispatch execution threads associated withthe shader programs via a thread dispatcher 1504. In one embodiment thethread dispatcher includes logic to arbitrate thread initiation requestsfrom the graphics and media pipelines and instantiate the requestedthreads on one or more execution unit in the execution units1508A-1508N. For example, the geometry pipeline (e.g., 1436 of FIG. 14)can dispatch vertex, tessellation, or geometry shaders to the threadexecution logic 1500 (FIG. 15) for processing. In some embodiments,thread dispatcher 1504 can also process runtime thread spawning requestsfrom the executing shader programs.

In some embodiments, the execution units 1508A-1508N support aninstruction set that includes native support for many standard 3Dgraphics shader instructions, such that shader programs from graphicslibraries (e.g., Direct 3D and OpenGL) are executed with a minimaltranslation. The execution units support vertex and geometry processing(e.g., vertex programs, geometry programs, vertex shaders), pixelprocessing (e.g., pixel shaders, fragment shaders) and general-purposeprocessing (e.g., compute and media shaders). Each of the executionunits 1508A-1508N is capable of multi-issue single instruction multipledata (SIMD) execution and multi-threaded operation enables an efficientexecution environment in the face of higher latency memory accesses.Each hardware thread within each execution unit has a dedicatedhigh-bandwidth register file and associated independent thread-state.Execution is multi-issue per clock to pipelines capable of integer,single and double precision floating point operations, SIMD branchcapability, logical operations, transcendental operations, and othermiscellaneous operations. While waiting for data from memory or one ofthe shared functions, dependency logic within the execution units1508A-1508N causes a waiting thread to sleep until the requested datahas been returned. While the waiting thread is sleeping, hardwareresources may be devoted to processing other threads. For example,during a delay associated with a vertex shader operation, an executionunit can perform operations for a pixel shader, fragment shader, oranother type of shader program, including a different vertex shader.

Each execution unit in execution units 1508A-1508N operates on arrays ofdata elements. The number of data elements is the “execution size,” orthe number of channels for the instruction. An execution channel is alogical unit of execution for data element access, masking, and flowcontrol within instructions. The number of channels may be independentof the number of physical Arithmetic Logic Units (ALUs) or FloatingPoint Units (FPUs) for a particular graphics processor. In someembodiments, execution units 1508A-1508N support integer andfloating-point data types.

The execution unit instruction set includes SIMD instructions. Thevarious data elements can be stored as a packed data type in a registerand the execution unit will process the various elements based on thedata size of the elements. For example, when operating on a 256-bit widevector, the 256 bits of the vector are stored in a register and theexecution unit operates on the vector as four separate 64-bit packeddata elements (Quad-Word (QW) size data elements), eight separate 32-bitpacked data elements (Double Word (DW) size data elements), sixteenseparate 16-bit packed data elements (Word (W) size data elements), orthirty-two separate 8-bit data elements (byte (B) size data elements).However, different vector widths and register sizes are possible.

One or more internal instruction caches (e.g., 1506) are included in thethread execution logic 1500 to cache thread instructions for theexecution units. In some embodiments, one or more data caches (e.g.,1512) are included to cache thread data during thread execution. In someembodiments, a sampler 1510 is included to provide texture sampling for3D operations and media sampling for media operations. In someembodiments, sampler 1510 includes specialized texture or media samplingfunctionality to process texture or media data during the samplingprocess before providing the sampled data to an execution unit.

During execution, the graphics and media pipelines send threadinitiation requests to thread execution logic 1500 via thread spawningand dispatch logic. Once a group of geometric objects has been processedand rasterized into pixel data, pixel processor logic (e.g., pixelshader logic, fragment shader logic, etc.) within the shader processor1502 is invoked to further compute output information and cause resultsto be written to output surfaces (e.g., color buffers, depth buffers,stencil buffers, etc.). In some embodiments, a pixel shader or fragmentshader calculates the values of the various vertex attributes that areto be interpolated across the rasterized object. In some embodiments,pixel processor logic within the shader processor 1502 then executes anapplication programming interface (API)-supplied pixel or fragmentshader program. To execute the shader program, the shader processor 1502dispatches threads to an execution unit (e.g., 1508A) via threaddispatcher 1504. In some embodiments, pixel shader 1502 uses texturesampling logic in the sampler 1510 to access texture data in texturemaps stored in memory. Arithmetic operations on the texture data and theinput geometry data compute pixel color data for each geometricfragment, or discards one or more pixels from further processing.

In some embodiments, the data port 1514 provides a memory accessmechanism for the thread execution logic 1500 output processed data tomemory for processing on a graphics processor output pipeline. In someembodiments, the data port 1514 includes or couples to one or more cachememories (e.g., data cache 1512) to cache data for memory access via thedata port.

FIG. 16 is a block diagram illustrating a graphics processor instructionformats 1600 according to some embodiments. In one or more embodiment,the graphics processor execution units support an instruction set havinginstructions in multiple formats. The solid lined boxes illustrate thecomponents that are generally included in an execution unit instruction,while the dashed lines include components that are optional or that areonly included in a sub-set of the instructions. In some embodiments,instruction format 1600 described and illustrated aremacro-instructions, in that they are instructions supplied to theexecution unit, as opposed to micro-operations resulting frominstruction decode once the instruction is processed.

In some embodiments, the graphics processor execution units nativelysupport instructions in a 128-bit instruction format 1610. A 64-bitcompacted instruction format 1630 is available for some instructionsbased on the selected instruction, instruction options, and number ofoperands. The native 128-bit instruction format 710 provides access toall instruction options, while some options and operations arerestricted in the 64-bit format 1630. The native instructions availablein the 64-bit format 1630 vary by embodiment. In some embodiments, theinstruction is compacted in part using a set of index values in an indexfield 1613. The execution unit hardware references a set of compactiontables based on the index values and uses the compaction table outputsto reconstruct a native instruction in the 128-bit instruction format1610.

For each format, instruction opcode 1612 defines the operation that theexecution unit is to perform. The execution units execute eachinstruction in parallel across the multiple data elements of eachoperand. For example, in response to an add instruction the executionunit performs a simultaneous add operation across each color channelrepresenting a texture element or picture element. By default, theexecution unit performs each instruction across all data channels of theoperands. In some embodiments, instruction control field 1614 enablescontrol over certain execution options, such as channels selection(e.g., predication) and data channel order (e.g., swizzle). Forinstructions in the 128-bit instruction format 1610 an exec-size field1616 limits the number of data channels that will be executed inparallel. In some embodiments, exec-size field 1616 is not available foruse in the 64-bit compact instruction format 1630.

Some execution unit instructions have up to three operands including twosource operands, src0 1620, src1 1622, and one destination 1618. In someembodiments, the execution units support dual destination instructions,where one of the destinations is implied. Data manipulation instructionscan have a third source operand (e.g., SRC2 1624), where the instructionopcode 1612 determines the number of source operands. An instruction'slast source operand can be an immediate (e.g., hard-coded) value passedwith the instruction.

In some embodiments, the 128-bit instruction format 1610 includes anaccess/address mode field 1626 specifying, for example, whether directregister addressing mode or indirect register addressing mode is used.When direct register addressing mode is used, the register address ofone or more operands is directly provided by bits in the instruction.

In some embodiments, the 128-bit instruction format 1610 includes anaccess/address mode field 1626, which specifies an address mode and/oran access mode for the instruction. In one embodiment the access mode isused to define a data access alignment for the instruction. Someembodiments support access modes including a 16-byte aligned access modeand a 1-byte aligned access mode, where the byte alignment of the accessmode determines the access alignment of the instruction operands. Forexample, when in a first mode, the instruction may use byte-alignedaddressing for source and destination operands and when in a secondmode, the instruction may use 16-byte-aligned addressing for all sourceand destination operands.

In one embodiment, the address mode portion of the access/address modefield 1626 determines whether the instruction is to use direct orindirect addressing. When direct register addressing mode is used bitsin the instruction directly provide the register address of one or moreoperands. When indirect register addressing mode is used, the registeraddress of one or more operands may be computed based on an addressregister value and an address immediate field in the instruction.

In some embodiments instructions are grouped based on opcode 1612bit-fields to simplify Opcode decode 1640. For an 8-bit opcode, bits 4,5, and 6 allow the execution unit to determine the type of opcode. Theprecise opcode grouping shown is merely an example. In some embodiments,a move and logic opcode group 1642 includes data movement and logicinstructions (e.g., move (mov), compare (cmp)). In some embodiments,move and logic group 1642 shares the five most significant bits (MSB),where move (mov) instructions are in the form of 0000xxxxb and logicinstructions are in the form of 0001xxxxb. A flow control instructiongroup 1644 (e.g., call, jump (jmp)) includes instructions in the form of0010xxxxb (e.g., 0x20). A miscellaneous instruction group 1646 includesa mix of instructions, including synchronization instructions (e.g.,wait, send) in the form of 0011xxxxb (e.g., 0x30). A parallel mathinstruction group 1648 includes component-wise arithmetic instructions(e.g., add, multiply (mul)) in the form of 0100xxxxb (e.g., 0x40). Theparallel math group 1648 performs the arithmetic operations in parallelacross data channels. The vector math group 1650 includes arithmeticinstructions (e.g., dp4) in the form of 0101xxxxb (e.g., 0x50). Thevector math group performs arithmetic such as dot product calculationson vector operands.

Additional Exemplary Graphics Pipeline

FIG. 17 is a block diagram of another embodiment of a graphics processor1700. Elements of FIG. 17 having the same reference numbers (or names)as the elements of any other figure herein can operate or function inany manner similar to that described elsewhere herein, but are notlimited to such.

In some embodiments, graphics processor 1700 includes a graphicspipeline 1720, a media pipeline 1730, a display engine 1740, threadexecution logic 1750, and a render output pipeline 1770. In someembodiments, graphics processor 1700 is a graphics processor within amulti-core processing system that includes one or more general purposeprocessing cores. The graphics processor is controlled by registerwrites to one or more control registers (not shown) or via commandsissued to graphics processor 1700 via a ring interconnect 1702. In someembodiments, ring interconnect 1702 couples graphics processor 1700 toother processing components, such as other graphics processors orgeneral-purpose processors. Commands from ring interconnect 1702 areinterpreted by a command streamer 1703, which supplies instructions toindividual components of graphics pipeline 1720 or media pipeline 1730.

In some embodiments, command streamer 1703 directs the operation of avertex fetcher 1705 that reads vertex data from memory and executesvertex-processing commands provided by command streamer 1703. In someembodiments, vertex fetcher 1705 provides vertex data to a vertex shader1707, which performs coordinate space transformation and lightingoperations to each vertex. In some embodiments, vertex fetcher 1705 andvertex shader 1707 execute vertex-processing instructions by dispatchingexecution threads to execution units 1752A-1752B via a thread dispatcher1731.

In some embodiments, execution units 1752A-1752B are an array of vectorprocessors having an instruction set for performing graphics and mediaoperations. In some embodiments, execution units 1752A-1752B have anattached L1 cache 1751 that is specific for each array or shared betweenthe arrays. The cache can be configured as a data cache, an instructioncache, or a single cache that is partitioned to contain data andinstructions in different partitions.

In some embodiments, graphics pipeline 1720 includes tessellationcomponents to perform hardware-accelerated tessellation of 3D objects.In some embodiments, a programmable hull shader 811 configures thetessellation operations. A programmable domain shader 817 providesback-end evaluation of tessellation output. A tessellator 1713 operatesat the direction of hull shader 1711 and contains special purpose logicto generate a set of detailed geometric objects based on a coarsegeometric model that is provided as input to graphics pipeline 1720. Insome embodiments, if tessellation is not used, tessellation components(e.g., hull shader 1711, tessellator 1713, and domain shader 1717) canbe bypassed.

In some embodiments, complete geometric objects can be processed by ageometry shader 1719 via one or more threads dispatched to executionunits 1752A-1752B, or can proceed directly to the clipper 1729. In someembodiments, the geometry shader operates on entire geometric objects,rather than vertices or patches of vertices as in previous stages of thegraphics pipeline. If the tessellation is disabled the geometry shader1719 receives input from the vertex shader 1707. In some embodiments,geometry shader 1719 is programmable by a geometry shader program toperform geometry tessellation if the tessellation units are disabled.

Before rasterization, a clipper 1729 processes vertex data. The clipper1729 may be a fixed function clipper or a programmable clipper havingclipping and geometry shader functions. In some embodiments, arasterizer and depth test component 1773 in the render output pipeline1770 dispatches pixel shaders to convert the geometric objects intotheir per pixel representations. In some embodiments, pixel shader logicis included in thread execution logic 1750. In some embodiments, anapplication can bypass the rasterizer and depth test component 1773 andaccess un-rasterized vertex data via a stream out unit 1723.

The graphics processor 1700 has an interconnect bus, interconnectfabric, or some other interconnect mechanism that allows data andmessage passing amongst the major components of the processor. In someembodiments, execution units 1752A-1752B and associated cache(s) 1751,texture and media sampler 1754, and texture/sampler cache 1758interconnect via a data port 1756 to perform memory access andcommunicate with render output pipeline components of the processor. Insome embodiments, sampler 1754, caches 1751, 1758 and execution units1752A-1752B each have separate memory access paths.

In some embodiments, render output pipeline 1770 contains a rasterizerand depth test component 1773 that converts vertex-based objects into anassociated pixel-based representation. In some embodiments, therasterizer logic includes a windower/masker unit to perform fixedfunction triangle and line rasterization. An associated render cache1778 and depth cache 1779 are also available in some embodiments. Apixel operations component 1777 performs pixel-based operations on thedata, though in some instances, pixel operations associated with 2Doperations (e.g. bit block image transfers with blending) are performedby the 2D engine 1741, or substituted at display time by the displaycontroller 1743 using overlay display planes. In some embodiments, ashared L3 cache 1775 is available to all graphics components, allowingthe sharing of data without the use of main system memory.

In some embodiments, graphics processor media pipeline 1730 includes amedia engine 1737 and a video front-end 1734. In some embodiments, videofront-end 1734 receives pipeline commands from the command streamer1703. In some embodiments, media pipeline 1730 includes a separatecommand streamer. In some embodiments, video front-end 1734 processesmedia commands before sending the command to the media engine 1737. Insome embodiments, media engine 1737 includes thread spawningfunctionality to spawn threads for dispatch to thread execution logic1750 via thread dispatcher 1731.

In some embodiments, graphics processor 1700 includes a display engine1740. In some embodiments, display engine 1740 is external to processor1700 and couples with the graphics processor via the ring interconnect1702, or some other interconnect bus or fabric. In some embodiments,display engine 1740 includes a 2D engine 1741 and a display controller1743. In some embodiments, display engine 1740 contains special purposelogic capable of operating independently of the 3D pipeline. In someembodiments, display controller 1743 couples with a display device (notshown), which may be a system integrated display device, as in a laptopcomputer, or an external display device attached via a display deviceconnector.

In some embodiments, graphics pipeline 1720 and media pipeline 1730 areconfigurable to perform operations based on multiple graphics and mediaprogramming interfaces and are not specific to any one applicationprogramming interface (API). In some embodiments, driver software forthe graphics processor translates API calls that are specific to aparticular graphics or media library into commands that can be processedby the graphics processor. In some embodiments, support is provided forthe Open Graphics Library (OpenGL), Open Computing Language (OpenCL),and/or Vulkan graphics and compute API, all from the Khronos Group. Insome embodiments, support may also be provided for the Direct3D libraryfrom the Microsoft Corporation. In some embodiments, a combination ofthese libraries may be supported. Support may also be provided for theOpen Source Computer Vision Library (OpenCV). A future API with acompatible 3D pipeline would also be supported if a mapping can be madefrom the pipeline of the future API to the pipeline of the graphicsprocessor.

Graphics Pipeline Programming

FIG. 18A is a block diagram illustrating a graphics processor commandformat 1800 according to some embodiments. FIG. 18B is a block diagramillustrating a graphics processor command sequence 1810 according to anembodiment. The solid lined boxes in FIG. 18A illustrate the componentsthat are generally included in a graphics command while the dashed linesinclude components that are optional or that are only included in asub-set of the graphics commands. The exemplary graphics processorcommand format 1800 of FIG. 18A includes data fields to identify atarget client 1802 of the command, a command operation code (opcode)1804, and the relevant data 1806 for the command. A sub-opcode 1805 anda command size 1808 are also included in some commands.

In some embodiments, client 1802 specifies the client unit of thegraphics device that processes the command data. In some embodiments, agraphics processor command parser examines the client field of eachcommand to condition the further processing of the command and route thecommand data to the appropriate client unit. In some embodiments, thegraphics processor client units include a memory interface unit, arender unit, a 2D unit, a 3D unit, and a media unit. Each client unithas a corresponding processing pipeline that processes the commands.Once the command is received by the client unit, the client unit readsthe opcode 1804 and, if present, sub-opcode 1805 to determine theoperation to perform. The client unit performs the command usinginformation in data field 1806. For some commands an explicit commandsize 1808 is expected to specify the size of the command. In someembodiments, the command parser automatically determines the size of atleast some of the commands based on the command opcode. In someembodiments commands are aligned via multiples of a double word.

The flow diagram in FIG. 18B shows an exemplary graphics processorcommand sequence 1810. In some embodiments, software or firmware of adata processing system that features an embodiment of a graphicsprocessor uses a version of the command sequence shown to set up,execute, and terminate a set of graphics operations. A sample commandsequence is shown and described for purposes of example only asembodiments are not limited to these specific commands or to thiscommand sequence. Moreover, the commands may be issued as batch ofcommands in a command sequence, such that the graphics processor willprocess the sequence of commands in at least partially concurrence.

In some embodiments, the graphics processor command sequence 1810 maybegin with a pipeline flush command 1812 to cause any active graphicspipeline to complete the currently pending commands for the pipeline. Insome embodiments, the 3D pipeline 1822 and the media pipeline 1824 donot operate concurrently. The pipeline flush is performed to cause theactive graphics pipeline to complete any pending commands. In responseto a pipeline flush, the command parser for the graphics processor willpause command processing until the active drawing engines completepending operations and the relevant read caches are invalidated.Optionally, any data in the render cache that is marked ‘dirty’ can beflushed to memory. In some embodiments, pipeline flush command 1812 canbe used for pipeline synchronization or before placing the graphicsprocessor into a low power state.

In some embodiments, a pipeline select command 1813 is used when acommand sequence requires the graphics processor to explicitly switchbetween pipelines. In some embodiments, a pipeline select command 1813is required only once within an execution context before issuingpipeline commands unless the context is to issue commands for bothpipelines. In some embodiments, a pipeline flush command 1812 isrequired immediately before a pipeline switch via the pipeline selectcommand 1813.

In some embodiments, a pipeline control command 1814 configures agraphics pipeline for operation and is used to program the 3D pipeline1822 and the media pipeline 1824. In some embodiments, pipeline controlcommand 1814 configures the pipeline state for the active pipeline. Inone embodiment, the pipeline control command 1814 is used for pipelinesynchronization and to clear data from one or more cache memories withinthe active pipeline before processing a batch of commands.

In some embodiments, return buffer state commands 1816 are used toconfigure a set of return buffers for the respective pipelines to writedata. Some pipeline operations require the allocation, selection, orconfiguration of one or more return buffers into which the operationswrite intermediate data during processing. In some embodiments, thegraphics processor also uses one or more return buffers to store outputdata and to perform cross thread communication. In some embodiments, thereturn buffer state 1816 includes selecting the size and number ofreturn buffers to use for a set of pipeline operations.

The remaining commands in the command sequence differ based on theactive pipeline for operations. Based on a pipeline determination 1820,the command sequence is tailored to the 3D pipeline 1822 beginning withthe 3D pipeline state 1830 or the media pipeline 1824 beginning at themedia pipeline state 1840.

The commands to configure the 3D pipeline state 1830 include 3D statesetting commands for vertex buffer state, vertex element state, constantcolor state, depth buffer state, and other state variables that are tobe configured before 3D primitive commands are processed. The values ofthese commands are determined at least in part based on the particular3D API in use. In some embodiments, 3D pipeline state 1830 commands arealso able to selectively disable or bypass certain pipeline elements ifthose elements will not be used.

In some embodiments, 3D primitive 1832 command is used to submit 3Dprimitives to be processed by the 3D pipeline. Commands and associatedparameters that are passed to the graphics processor via the 3Dprimitive 1832 command are forwarded to the vertex fetch function in thegraphics pipeline. The vertex fetch function uses the 3D primitive 1832command data to generate vertex data structures. The vertex datastructures are stored in one or more return buffers. In someembodiments, 3D primitive 1832 command is used to perform vertexoperations on 3D primitives via vertex shaders. To process vertexshaders, 3D pipeline 1822 dispatches shader execution threads tographics processor execution units.

In some embodiments, 3D pipeline 1822 is triggered via an execute 1834command or event. In some embodiments, a register write triggers commandexecution. In some embodiments execution is triggered via a ‘go’ or‘kick’ command in the command sequence. In one embodiment, commandexecution is triggered using a pipeline synchronization command to flushthe command sequence through the graphics pipeline. The 3D pipeline willperform geometry processing for the 3D primitives. Once operations arecomplete, the resulting geometric objects are rasterized and the pixelengine colors the resulting pixels. Additional commands to control pixelshading and pixel back end operations may also be included for thoseoperations.

In some embodiments, the graphics processor command sequence 1810follows the media pipeline 1824 path when performing media operations.In general, the specific use and manner of programming for the mediapipeline 1824 depends on the media or compute operations to beperformed. Specific media decode operations may be offloaded to themedia pipeline during media decode. In some embodiments, the mediapipeline can also be bypassed and media decode can be performed in wholeor in part using resources provided by one or more general purposeprocessing cores. In one embodiment, the media pipeline also includeselements for general-purpose graphics processor unit (GPGPU) operations,where the graphics processor is used to perform SIMD vector operationsusing computational shader programs that are not explicitly related tothe rendering of graphics primitives.

In some embodiments, media pipeline 1824 is configured in a similarmanner as the 3D pipeline 1822. A set of commands to configure the mediapipeline state 1840 are dispatched or placed into a command queue beforethe media object commands 1842. In some embodiments, commands for themedia pipeline state 1840 include data to configure the media pipelineelements that will be used to process the media objects. This includesdata to configure the video decode and video encode logic within themedia pipeline, such as encode or decode format. In some embodiments,commands for the media pipeline state 1840 also support the use of oneor more pointers to “indirect” state elements that contain a batch ofstate settings.

In some embodiments, media object commands 1842 supply pointers to mediaobjects for processing by the media pipeline. The media objects includememory buffers containing video data to be processed. In someembodiments, all media pipeline states must be valid before issuing amedia object command 1842. Once the pipeline state is configured andmedia object commands 1842 are queued, the media pipeline 1824 istriggered via an execute command 1844 or an equivalent execute event(e.g., register write). Output from media pipeline 1824 may then be postprocessed by operations provided by the 3D pipeline 1822 or the mediapipeline 1824. In some embodiments, GPGPU operations are configured andexecuted in a similar manner as media operations.

Graphics Software Architecture

FIG. 19 illustrates exemplary graphics software architecture for a dataprocessing system 1900 according to some embodiments. In someembodiments, software architecture includes a 3D graphics application1910, an operating system 1920, and at least one processor 1930. In someembodiments, processor 1930 includes a graphics processor 1932 and oneor more general-purpose processor core(s) 1934. The graphics application1910 and operating system 1920 each execute in the system memory 1950 ofthe data processing system.

In some embodiments, 3D graphics application 1910 contains one or moreshader programs including shader instructions 1912. The shader languageinstructions may be in a high-level shader language, such as the HighLevel Shader Language (HLSL) or the OpenGL Shader Language (GLSL). Theapplication also includes executable instructions 1914 in a machinelanguage suitable for execution by the general-purpose processor core1934. The application also includes graphics objects 1916 defined byvertex data.

In some embodiments, operating system 1920 is a Microsoft® Windows®operating system from the Microsoft Corporation, a proprietary UNIX-likeoperating system, or an open source UNIX-like operating system using avariant of the Linux kernel. The operating system 1920 can support agraphics API 1922 such as the Direct3D API, the OpenGL API, or theVulkan API. When the Direct3D API is in use, the operating system 1920uses a front-end shader compiler 1924 to compile any shader instructions1912 in HLSL into a lower-level shader language. The compilation may bea just-in-time (JIT) compilation or the application can perform shaderpre-compilation. In some embodiments, high-level shaders are compiledinto low-level shaders during the compilation of the 3D graphicsapplication 1910. In some embodiments, the shader instructions 1912 areprovided in an intermediate form, such as a version of the StandardPortable Intermediate Representation (SPIR) used by the Vulkan API.

In some embodiments, user mode graphics driver 1926 contains a back-endshader compiler 1927 to convert the shader instructions 1912 into ahardware specific representation. When the OpenGL API is in use, shaderinstructions 1912 in the GLSL high-level language are passed to a usermode graphics driver 1926 for compilation. In some embodiments, usermode graphics driver 1926 uses operating system kernel mode functions1928 to communicate with a kernel mode graphics driver 1929. In someembodiments, kernel mode graphics driver 1929 communicates with graphicsprocessor 1932 to dispatch commands and instructions.

IP Core Implementations

One or more aspects of at least one embodiment may be implemented byrepresentative code stored on a machine-readable medium which representsand/or defines logic within an integrated circuit such as a processor.For example, the machine-readable medium may include instructions whichrepresent various logic within the processor. When read by a machine,the instructions may cause the machine to fabricate the logic to performthe techniques described herein. Such representations, known as “IPcores,” are reusable units of logic for an integrated circuit that maybe stored on a tangible, machine-readable medium as a hardware modelthat describes the structure of the integrated circuit. The hardwaremodel may be supplied to various customers or manufacturing facilities,which load the hardware model on fabrication machines that manufacturethe integrated circuit. The integrated circuit may be fabricated suchthat the circuit performs operations described in association with anyof the embodiments described herein.

FIG. 20 is a block diagram illustrating an IP core development system2000 that may be used to manufacture an integrated circuit to performoperations according to an embodiment. The IP core development system2000 may be used to generate modular, re-usable designs that can beincorporated into a larger design or used to construct an entireintegrated circuit (e.g., an SOC integrated circuit). A design facility2030 can generate a software simulation 2010 of an IP core design in ahigh level programming language (e.g., C/C++). The software simulation2010 can be used to design, test, and verify the behavior of the IP coreusing a simulation model 2012. The simulation model 2012 may includefunctional, behavioral, and/or timing simulations. A register transferlevel (RTL) design 2015 can then be created or synthesized from thesimulation model 2012. The RTL design 2015 is an abstraction of thebehavior of the integrated circuit that models the flow of digitalsignals between hardware registers, including the associated logicperformed using the modeled digital signals. In addition to an RTLdesign 2015, lower-level designs at the logic level or transistor levelmay also be created, designed, or synthesized. Thus, the particulardetails of the initial design and simulation may vary.

The RTL design 2015 or equivalent may be further synthesized by thedesign facility into a hardware model 2020, which may be in a hardwaredescription language (HDL), or some other representation of physicaldesign data. The HDL may be further simulated or tested to verify the IPcore design. The IP core design can be stored for delivery to a 3^(rd)party fabrication facility 2065 using non-volatile memory 2040 (e.g.,hard disk, flash memory, or any non-volatile storage medium).Alternatively, the IP core design may be transmitted (e.g., via theInternet) over a wired connection 2050 or wireless connection 2060. Thefabrication facility 2065 may then fabricate an integrated circuit thatis based at least in part on the IP core design. The fabricatedintegrated circuit can be configured to perform operations in accordancewith at least one embodiment described herein.

Exemplary System on a Chip Integrated Circuit

FIG. 21-23 illustrated exemplary integrated circuits and associatedgraphics processors that may be fabricated using one or more IP cores,according to various embodiments described herein. In addition to whatis illustrated, other logic and circuits may be included, includingadditional graphics processors/cores, peripheral interface controllers,or general purpose processor cores.

FIG. 21 is a block diagram illustrating an exemplary system on a chipintegrated circuit 2100 that may be fabricated using one or more IPcores, according to an embodiment. Exemplary integrated circuit 2100includes one or more application processor(s) 2105 (e.g., CPUs), atleast one graphics processor 2110, and may additionally include an imageprocessor 2115 and/or a video processor 2120, any of which may be amodular IP core from the same or multiple different design facilities.Integrated circuit 2100 includes peripheral or bus logic including a USBcontroller 2125, UART controller 2130, an SPI/SDIO controller 2135, andan I²S/I²C controller 2140. Additionally, the integrated circuit caninclude a display device 2145 coupled to one or more of ahigh-definition multimedia interface (HDMI) controller 2150 and a mobileindustry processor interface (MIPI) display interface 2155. Storage maybe provided by a flash memory subsystem 2160 including flash memory anda flash memory controller. Memory interface may be provided via a memorycontroller 2165 for access to SDRAM or SRAM memory devices. Someintegrated circuits additionally include an embedded security engine2170.

FIG. 22 is a block diagram illustrating an exemplary graphics processor2210 of a system on a chip integrated circuit that may be fabricatedusing one or more IP cores, according to an embodiment. Graphicsprocessor 2210 can be a variant of the graphics processor 2110 of FIG.21. Graphics processor 2210 includes a vertex processor 2205 and one ormore fragment processor(s) 2215A-2215N (e.g., 2215A, 2215B, 2215C,2215D, through 2215N-1, and 2215N). Graphics processor 2210 can executedifferent shader programs via separate logic, such that the vertexprocessor 2205 is optimized to execute operations for vertex shaderprograms, while the one or more fragment processor(s) 2215A-2215Nexecute fragment (e.g., pixel) shading operations for fragment or pixelshader programs. The vertex processor 2205 performs the vertexprocessing stage of the 3D graphics pipeline and generates primitivesand vertex data. The fragment processor(s) 2215A-2215N use the primitiveand vertex data generated by the vertex processor 2205 to produce aframebuffer that is displayed on a display device. In one embodiment,the fragment processor(s) 2215A-2215N are optimized to execute fragmentshader programs as provided for in the OpenGL API, which may be used toperform similar operations as a pixel shader program as provided for inthe Direct 3D API.

Graphics processor 2210 additionally includes one or more memorymanagement units (MMUs) 2220A-2220B, cache(s) 2225A-2225B, and circuitinterconnect(s) 2230A-2230B. The one or more MMU(s) 2220A-2220B providefor virtual to physical address mapping for integrated circuit 2210,including for the vertex processor 2205 and/or fragment processor(s)2215A-2215N, which may reference vertex or image/texture data stored inmemory, in addition to vertex or image/texture data stored in the one ormore cache(s) 2225A-2225B. In one embodiment the one or more MMU(s)2220A-2220B may be synchronized with other MMUs within the system,including one or more MMUs associated with the one or more applicationprocessor(s) 2105, image processor 2115, and/or video processor 2120 ofFIG. 21, such that each processor 2105-2120 can participate in a sharedor unified virtual memory system. The one or more circuitinterconnect(s) 2230A-2230B enable graphics processor 2210 to interfacewith other IP cores within the SoC, either via an internal bus of theSoC or via a direct connection, according to embodiments.

FIG. 23 is a block diagram illustrating an additional exemplary graphicsprocessor 2310 of a system on a chip integrated circuit that may befabricated using one or more IP cores, according to an embodiment.Graphics processor 2310 can be a variant of the graphics processor 2110of FIG. 21. Graphics processor 2310 includes the one or more MMU(s)2220A-2220B, caches 2225A-2225B, and circuit interconnect(s) 2230A-2230Bof the integrated circuit 2200 of FIG. 22.

Graphics processor 2310 includes one or more shader core(s) 2315A-2315N(e.g., 2315A, 2315B, 2315C, 2315D, 2315E, 2315F, through 2315N-1, and2315N), which provides for a unified shader core architecture in which asingle core or type or core can execute all types of programmable shadercode, including shader program code to implement vertex shaders,fragment shaders, and/or compute shaders. The exact number of shadercores present can vary among embodiments and implementations.Additionally, graphics processor 2310 includes an inter-core taskmanager 2305, which acts as a thread dispatcher to dispatch executionthreads to one or more shader core(s) 2315A-2315N and a tiling unit 2318to accelerate tiling operations for tile-based rendering, in whichrendering operations for a scene are subdivided in image space, forexample to exploit local spatial coherence within a scene or to optimizeuse of internal caches.

The following clauses and/or examples pertain to specific embodiments orexamples thereof. Specifics in the examples may be used anywhere in oneor more embodiments. The various features of the different embodimentsor examples may be variously combined with some features included andothers excluded to suit a variety of different applications. Examplesmay include subject matter such as a method, means for performing actsof the method, at least one machine-readable medium includinginstructions that, when performed by a machine cause the machine toperform acts of the method, or of an apparatus or system according toembodiments and examples described herein. Various components can be ameans for performing the operations or functions described.

One embodiment provides for a general-purpose graphics processing devicecomprising a general-purpose graphics processing compute block toprocess a workload including graphics or compute operations, a firstcache memory, and a coherency module enable the first cache memory tocoherently cache data for the workload, the data stored in memory withina virtual address space, wherein the virtual address space shared with aseparate general-purpose processor including a second cache memory thatis coherent with the first cache memory. In one embodiment the computeblock includes multiple compute clusters, each compute cluster includingmultiple graphics multiprocessors. The first cache memory can be a level3 cache memory. The coherency module tracks coherency at superlinegranularity, wherein a superline includes multiple cache lines. Datastorage for the first cache memory can be managed at cache linegranularity while coherence between the first cache memory and thesecond cache memory is managed at superline granularity.

In one embodiment, before a write of data to a cache line within thefirst cache memory, the coherency module is to determine an ownershipstatus for the superline associated with the cache line. To takeownership of a superline associated with the cache line, the coherencymodule can broadcast a region snoop to take ownership of the superlineassociated with the cache line. In one embodiment the general-purposegraphics processing device is an add-in card connected to the separategeneral-purpose processor via a system bus.

One embodiment provides for a method on a heterogeneous processingsystem, the method comprising receiving a request to access a virtualmemory address from a process executing on an agent of the heterogeneousprocessing system; determining if the agent has ownership of a firstsuperline associated with the virtual address, the first superlineassociated with a memory region spanning multiple cache lines; accessingthe virtual memory address from the agent without triggering a snooprequest when the agent has ownership of the first superline; and sendinga region snoop request to acquire ownership of the first superline whenthe agent does not have ownership of the superline. In one embodiment ifthe agent has ownership of a first superline includes reading an entryfor the first superline from an on-die superline directory table cache.Determining if the agent has ownership of a first superline includesreading an entry for the first superline from a superline directorytable in memory when the agent is a general-purpose processor. When theagent is a general-purpose graphics processing unit, determining if theagent has ownership of a first superline includes reading an entry forthe first superline from a superline ownership table within memory ofthe general-purpose graphics processing unit.

One embodiment provides for a heterogeneous data processing systemcomprising a general-purpose processor including a first cache memoryand a first coherency module and a general-purpose graphics processorincluding a second cache memory and a second coherency module, whereinthe first coherency module and the second coherency module enableheterogeneous coherency between the first cache memory and the secondcache memory, the heterogeneous coherency enabled at multiple cache linegranularity. The heterogeneous data processing system additionallyincludes a first memory module to store a superline directory table, thesuperline directory table to track ownership for each superline owned bythe general-purpose processor and the general-purpose processor, whereineach superline is an address region that spans multiple cache lines ofthe first cache memory and the second cache memory.

The embodiments described herein refer to specific configurations ofhardware, such as application specific integrated circuits (ASICs),configured to perform certain operations or having a predeterminedfunctionality. Such electronic devices typically include a set of one ormore processors coupled to one or more other components, such as one ormore storage devices (non-transitory machine-readable storage media),user input/output devices (e.g., a keyboard, a touchscreen, and/or adisplay), and network connections. The coupling of the set of processorsand other components is typically through one or more busses and bridges(also termed as bus controllers). The storage device and signalscarrying the network traffic respectively represent one or moremachine-readable storage media and machine-readable communication media.Thus, the storage devices of a given electronic device typically storecode and/or data for execution on the set of one or more processors ofthat electronic device.

Of course, one or more parts of an embodiment may be implemented usingdifferent combinations of software, firmware, and/or hardware.Throughout this detailed description, for the purposes of explanation,numerous specific details were set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however, toone skilled in the art that the embodiments may be practiced withoutsome of these specific details. In certain instances, well-knownstructures and functions were not described in elaborate detail to avoidobscuring the inventive subject matter of the embodiments. Accordingly,the scope and spirit of the invention should be judged in terms of theclaims that follow.

What is claimed is:
 1. An electronic device comprising: a general-purpose processor including a first cache memory and a first coherency module; and a general-purpose graphics processor including a second cache memory and a second coherency module, wherein the first coherency module and the second coherency module enable heterogeneous coherency between the first cache memory and the second cache memory, the heterogeneous coherency enabled at multiple cache line granularity; and one or more memory controllers coupled with the general-purpose processor and the general-purpose graphics processor, the one or more memory controllers to enable communication with a memory module to store a superline directory table, wherein the superline directory table is to track ownership for a superline owned by the general-purpose processor and the general-purpose graphics processor, wherein the superline is a sub-page address region that spans multiple cache lines of the first cache memory and the second cache memory.
 2. The electronic device as in claim 1, wherein data storage for the first cache memory is managed at cache line granularity, coherence for sub-page shared virtual memory allocations cached by the first cache memory, and the second cache memory are managed at superline granularity.
 3. The electronic device as in claim 2, wherein the first cache memory is a level 3 cache memory.
 4. The electronic device as in claim 3, wherein the second cache memory is a last level cache coupled with the general-purpose processor and the general-purpose graphics processor.
 5. The electronic device as in claim 4, wherein the general-purpose graphics processor includes a superline ownership table to store a set of superlines owned by the general-purpose graphics processor, wherein the superline ownership table includes an entry for each superline in the set of superlines owned by the general-purpose graphics processor and each entry in the superline ownership table includes a superline tag and a coherency protocol status for the superline.
 6. The electronic device as in claim 5, wherein the coherency protocol status is one of modified, exclusive, shared, or invalid and each entry in the superline ownership table additionally includes a valid bit for each cache line within the superline.
 7. The electronic device as in claim 1, wherein the general-purpose graphics processor additionally includes a graphics processing compute block including multiple graphics multiprocessors.
 8. The electronic device as in claim 7, wherein multiple graphics multiprocessors are to process a workload including graphics or compute operations.
 9. The electronic device as in claim 8, wherein the workload is a heterogeneous workload including operations to be performed by the general-purpose graphics processing compute block and the general-purpose processor.
 10. The electronic device as in claim 9, wherein the operations of the workload are to access a unified memory address space and the unified memory address space includes system memory and graphics processor memory.
 11. The electronic device as in claim 10, wherein the general-purpose graphics processor is an add-in card connected to the general-purpose processor via a system bus and the add-in card includes the graphics processor memory.
 12. A data processing system comprising: a general-purpose processor including a first cache memory and a first coherency module; a system bus coupled with the general-purpose processor; and a general-purpose graphics processor coupled with general-purpose processor via the system bus, the general-purpose graphics processor including a second cache memory and a second coherency module, wherein the first coherency module and the second coherency module enable heterogeneous coherency between the first cache memory and the second cache memory, the heterogeneous coherency enabled at multiple cache line granularity; and a memory module to store a superline directory table, wherein the superline directory table is to track ownership for a superline owned by the general-purpose processor and the general-purpose graphics processor, wherein the superline is a sub-page address region that spans multiple cache lines of the first cache memory and the second cache memory.
 13. The data processing system as in claim 12, wherein data storage for the first cache memory is managed at cache line granularity, coherence for sub-page shared virtual memory allocations cached by the first cache memory, and the second cache memory are managed at superline granularity.
 14. The data processing system as in claim 13, wherein the first cache memory is a level 3 cache memory.
 15. The data processing system as in claim 14, wherein the second cache memory is a last level cache coupled with the general-purpose processor and the general-purpose graphics processor.
 16. The data processing system as in claim 15, wherein the general-purpose graphics processor includes a superline ownership table to store a set of superlines owned by the general-purpose graphics processor, wherein the superline ownership table includes an entry for each superline in the set of superlines owned by the general-purpose graphics processor and each entry in the superline ownership table includes a superline tag and a coherency protocol status for the superline.
 17. The data processing system as in claim 16, wherein the coherency protocol status is one of modified, exclusive, shared, or invalid and each entry in the superline ownership table additionally includes a valid bit for each cache line within the superline.
 18. The data processing system as in claim 12, wherein the general-purpose graphics processor additionally includes a graphics processing compute block including multiple graphics multiprocessors.
 19. The data processing system as in claim 18, wherein multiple graphics multiprocessors are to process a workload including graphics or compute operations.
 20. The data processing system as in claim 19, wherein the workload is a heterogeneous workload including operations to be performed by the general-purpose graphics processing compute block and the general-purpose processor, wherein the operations of the workload are to access a unified memory address space including system memory and graphics processor memory. 